correlation does not imply causation

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pages: 250 words: 64,011

Everydata: The Misinformation Hidden in the Little Data You Consume Every Day by John H. Johnson

Affordable Care Act / Obamacare, Black Swan, business intelligence, Carmen Reinhart, cognitive bias, correlation does not imply causation, Daniel Kahneman / Amos Tversky, data science, Donald Trump, en.wikipedia.org, Kenneth Rogoff, labor-force participation, lake wobegon effect, Long Term Capital Management, Mercator projection, Mercator projection distort size, especially Greenland and Africa, meta-analysis, Nate Silver, obamacare, p-value, PageRank, pattern recognition, publication bias, QR code, randomized controlled trial, risk-adjusted returns, Ronald Reagan, selection bias, statistical model, The Signal and the Noise by Nate Silver, Thomas Bayes, Tim Cook: Apple, wikimedia commons, Yogi Berra

Unfortunately, we don’t have the space to teach you how to run a perfect statistical analysis, or determine the exact correlation. But that’s okay, because our goal is simply to help you make better decisions by recognizing the difference between correlation and causation, and understanding some of the reasons that people confuse the two—so you can avoid making the same mistakes. How to Be a Good Consumer of Correlation and Causation So now, armed with a better understanding of the distinction between correlation and causation, here are some steps to keep in mind when consuming data about a statistical relationship: 1. Ask yourself what is being represented in the news article or research.

We’ve just read a lot of studies and media reports that seem to draw the wrong conclusion from statistical analyses—specifically, reports and articles that confuse correlation with causation, and therefore, sometimes unintentionally, mislead the reader about the key takeaways. It’s important to note that there are two issues here: first of all, there are the original scientific studies that sometimes confuse correlation with causation. But what you’re more likely to encounter in your everyday life are newspaper articles and other media accounts that misreport the findings from valid scientific studies.

These types of connections—when there is some sort of relationship between data—are called correlations. But, as we’ll explore in this chapter, the mere existence of such a statistical relationship between two factors does not imply that there is actually a meaningful link between them. Correlation does not equal causation. It’s actually one of the most common ways that people misinterpret data. But don’t worry—in this chapter, we’ll take a close look at how and why people mistake correlation for causation, and give you the tools to help you understand which everydata you should really believe. SMARTPHONES = SMART PEOPLE? So, back to the smart people analysis. We dug a bit deeper into what the actual studies said, and uncovered some interesting caveats, warnings, and facts that might shed some light on these findings.


pages: 533 words: 125,495

Rationality: What It Is, Why It Seems Scarce, Why It Matters by Steven Pinker

affirmative action, Albert Einstein, autonomous vehicles, availability heuristic, Ayatollah Khomeini, backpropagation, basic income, belling the cat, butterfly effect, Cass Sunstein, choice architecture, clean water, coronavirus, correlation coefficient, correlation does not imply causation, COVID-19, critical race theory, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Attenborough, defund the police, delayed gratification, disinformation, Donald Trump, Dr. Strangelove, effective altruism, en.wikipedia.org, Erdős number, Estimating the Reproducibility of Psychological Science, feminist movement, framing effect, George Akerlof, George Floyd, germ theory of disease, high batting average, index card, Jeff Bezos, job automation, John Nash: game theory, John von Neumann, libertarian paternalism, longitudinal study, loss aversion, Mahatma Gandhi, meta-analysis, microaggression, Monty Hall problem, Nash equilibrium, New Journalism, Paul Erdős, Paul Samuelson, Peter Singer: altruism, Pierre-Simon Laplace, placebo effect, QAnon, QWERTY keyboard, Ralph Waldo Emerson, randomized controlled trial, replication crisis, Richard Thaler, scientific worldview, selection bias, social discount rate, Stanford marshmallow experiment, Steve Bannon, Steven Pinker, sunk-cost fallacy, the scientific method, Thomas Bayes, Tragedy of the Commons, twin studies, universal basic income, Upton Sinclair, urban planning, Walter Mischel, yellow journalism, zero-sum game

That’s because low birth weight must be caused by something, and the other possible causes, such as alcohol or drug abuse, may be even more harmful to the child.20 The collider fallacy also explains why Jenny Cavilleri unfairly maintained that rich boys are stupid: to get into Harvard (B), you can be either rich (A) or smart (C). From Correlation to Causation: Real and Natural Experiments Now that we’ve probed the nature of correlation and the nature of causation, it’s time to see how to get from one to the other. The problem is not that “correlation does not imply causation.” It usually does, because unless the correlation is illusory or a coincidence, something must have caused one variable to align with the other. The problem is that when one thing is correlated with another, it does not necessarily mean that the first caused the second.

As Thomas Hobbes put it, the fundamental principle of society is “that a man be willing, when others are so too . . . to lay down this right to all things; and be contented with so much liberty against other men, as he would allow other men against himself.”22 This social contract does not just embody the moral logic of impartiality. It also removes wicked temptations, sucker’s payoffs, and tragedies of mutual defection. 9 CORRELATION AND CAUSATION One of the first things taught in introductory statistics textbooks is that correlation is not causation. It is also one of the first things forgotten. —Thomas Sowell1 Rationality embraces all spheres of life, including the personal, the political, and the scientific. It’s not surprising that the Enlightenment-inspired theorists of American democracy were fanboys of science, nor that real and wannabe autocrats latch onto harebrained theories of cause and effect.2 Mao Zedong forced Chinese farmers to crowd their seedlings together to enhance their socialist solidarity, and a recent American leader suggested that Covid-19 could be treated with injections of bleach.

One of my grandfather’s favorite jokes was about the man who gorged himself on cholent (the meat and bean stew simmered for twelve hours during the Sabbath blackout on cooking) with a glass of tea, and then lay in pain moaning that the tea had made him sick. Presumably you had to have been born in Poland in 1900 to find this as uproarious as he did, but if you get the joke at all, you can see how the difference between correlation and causation is part of our common sense. Nonetheless, Niyazovian confusions are common in our public discourse. This chapter probes the nature of correlation, the nature of causation, and the ways to tell the difference. What Is Correlation? A correlation is a dependence of the value of one variable on the value of another: if you know one, you can predict the other, at least approximately.


The Book of Why: The New Science of Cause and Effect by Judea Pearl, Dana Mackenzie

affirmative action, Albert Einstein, Asilomar, Bayesian statistics, computer age, computer vision, Computing Machinery and Intelligence, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, data science, Edmond Halley, Elon Musk, en.wikipedia.org, experimental subject, Gregor Mendel, Isaac Newton, iterative process, John Snow's cholera map, Loebner Prize, loose coupling, Louis Pasteur, Menlo Park, Monty Hall problem, pattern recognition, Paul Erdős, personalized medicine, Pierre-Simon Laplace, placebo effect, prisoner's dilemma, probability theory / Blaise Pascal / Pierre de Fermat, randomized controlled trial, Recombinant DNA, selection bias, self-driving car, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steve Jobs, strong AI, The Design of Experiments, the scientific method, Thomas Bayes, Turing test

And when you prohibit speech, you prohibit thought and stifle principles, methods, and tools. Readers do not have to be scientists to witness this prohibition. In Statistics 101, every student learns to chant, “Correlation is not causation.” With good reason! The rooster’s crow is highly correlated with the sunrise; yet it does not cause the sunrise. Unfortunately, statistics has fetishized this commonsense observation. It tells us that correlation is not causation, but it does not tell us what causation is. In vain will you search the index of a statistics textbook for an entry on “cause.” Students are not allowed to say that X is the cause of Y—only that X and Y are “related” or “associated.”

Having built this bridge, Wright could travel backward over it, from the correlations measured in the data (rung one) to the hidden causal quantities, d and h (rung two). He did this by solving algebraic equations. This idea must have seemed simple to Wright but turned out to be revolutionary because it was the first proof that the mantra “Correlation does not imply causation” should give way to “Some correlations do imply causation.” FIGURE 2.7. Sewall Wright’s first path diagram, illustrating the factors leading to coat color in guinea pigs. D = developmental factors (after conception, before birth), E = environmental factors (after birth), G = genetic factors from each individual parent, H = combined hereditary factors from both parents, O, O′ = offspring.

In reality you conditioned on a collider by censoring all the tails-tails outcomes. In The Direction of Time, published posthumously in 1956, philosopher Hans Reichenbach made a daring conjecture called the “common cause principle.” Rebutting the adage “Correlation does not imply causation,” Reichenbach posited a much stronger idea: “No correlation without causation.” He meant that a correlation between two variables, X and Y, cannot come about by accident. Either one of the variables causes the other, or a third variable, say Z, precedes and causes them both. Our simple coin-flip experiment proves that Reichenbach’s dictum was too strong, because it neglects to account for the process by which observations are selected.


pages: 579 words: 76,657

Data Science from Scratch: First Principles with Python by Joel Grus

backpropagation, correlation does not imply causation, data science, Hacker News, natural language processing, Netflix Prize, p-value, Paul Graham, recommendation engine, SpamAssassin, statistical model

In addition, correlation tells you nothing about how large the relationship is. The variables: x = [-2, 1, 0, 1, 2] y = [99.98, 99.99, 100, 100.01, 100.02] are perfectly correlated, but (depending on what you’re measuring) it’s quite possible that this relationship isn’t all that interesting. Correlation and Causation You have probably heard at some point that “correlation is not causation,” most likely by someone looking at data that posed a challenge to parts of his worldview that he was reluctant to question. Nonetheless, this is an important point — if x and y are strongly correlated, that might mean that x causes y, that y causes x, that each causes the other, that some third factor causes both, or it might mean nothing.

closeness centrality, Betweenness Centrality clustering, Clustering-For Further Explorationbottom-up hierarchical clustering, Bottom-up Hierarchical Clustering-Bottom-up Hierarchical Clustering choosing k, Choosing k example, clustering colors, Example: Clustering Colors example, meetups, Example: Meetups-Example: Meetups k-means clustering, The Model clusters, Rescaling, The Ideadistance between, Bottom-up Hierarchical Clustering code examples from this book, Using Code Examples coefficient of determination, The Model combiners (in MapReduce), An Aside: Combiners comma-separated values files, Delimited Filescleaning comma-delimited stock prices, Cleaning and Munging command line, running Python scripts at, stdin and stdout conditional probability, Conditional Probabilityrandom variables and, Random Variables confidence intervals, Confidence Intervals confounding variables, Simpson’s Paradox confusion matrix, Correctness continue statement (Python), Control Flow continuity correction, Example: Flipping a Coin continuous distributions, Continuous Distributions control flow (in Python), Control Flow correctness, Correctness correlation, Correlationand causation, Correlation and Causation in simple linear regression, The Model other caveats, Some Other Correlational Caveats outliers and, Correlation Simpson's Paradox and, Simpson’s Paradox correlation function, Simple Linear Regression cosine similarity, User-Based Collaborative Filtering, Item-Based Collaborative Filtering Counter (Python), Counter covariance, Correlation CREATE TABLE statement (SQL), CREATE TABLE and INSERT cumulative distribution function (cdf), Continuous Distributions currying (Python), Functional Tools curse of dimensionality, The Curse of Dimensionality-The Curse of Dimensionality, User-Based Collaborative Filtering D D3.js library, Visualization datacleaning and munging, Cleaning and Munging exploring, Exploring Your Data-Many Dimensions finding, Find Data getting, Getting Data-For Further Explorationreading files, Reading Files-Delimited Files scraping from web pages, Scraping the Web-Example: O’Reilly Books About Data using APIs, Using APIs-Using Twython using stdin and stdout, stdin and stdout manipulating, Manipulating Data-Manipulating Data rescaling, Rescaling-Rescaling data mining, What Is Machine Learning?

Index A A/B test, Example: Running an A/B Test accuracy, Correctnessof model performance, Correctness all function (Python), Truthiness Anaconda distribution of Python, Getting Python any function (Python), Truthiness APIs, using to get data, Using APIs-Using Twythonexample, using Twitter APIs, Example: Using the Twitter APIs-Using Twythongetting credentials, Getting Credentials using twython, Using Twython finding APIs, Finding APIs JSON (and XML), JSON (and XML) unauthenticated API, Using an Unauthenticated API args and kwargs (Python), args and kwargs argument unpacking, zip and Argument Unpacking arithmeticin Python, Arithmetic performing on vectors, Vectors artificial neural networks, Neural Networks(see also neural networks) assignment, multiple, in Python, Tuples B backpropagation, Backpropagation bagging, Random Forests bar charts, Bar Charts-Line Charts Bayes's Theorem, Bayes’s Theorem, A Really Dumb Spam Filter Bayesian Inference, Bayesian Inference Beautiful Soup library, HTML and the Parsing Thereof, n-gram Modelsusing with XML data, JSON (and XML) Bernoulli trial, Example: Flipping a Coin Beta distributions, Bayesian Inference betweenness centrality, Betweenness Centrality-Betweenness Centrality bias, The Bias-Variance Trade-offadditional data and, The Bias-Variance Trade-off bigram model, n-gram Models binary relationships, representing with matrices, Matrices binomial random variables, The Central Limit Theorem, Example: Flipping a Coin Bokeh project, Visualization booleans (Python), Truthiness bootstrap aggregating, Random Forests bootstrapping data, Digression: The Bootstrap bottom-up hierarchical clustering, Bottom-up Hierarchical Clustering-Bottom-up Hierarchical Clustering break statement (Python), Control Flow buckets, grouping data into, Exploring One-Dimensional Data business models, Modeling C CAPTCHA, defeating with a neural network, Example: Defeating a CAPTCHA-Example: Defeating a CAPTCHA causation, correlation and, Correlation and Causation, The Model cdf (see cumulative distribtion function) central limit theorem, The Central Limit Theorem, Confidence Intervals central tendenciesmean, Central Tendencies median, Central Tendencies mode, Central Tendencies quantile, Central Tendencies centralitybetweenness, Betweenness Centrality-Betweenness Centrality closeness, Betweenness Centrality degree, Finding Key Connectors, Betweenness Centrality eigenvector, Eigenvector Centrality-Centrality classes (Python), Object-Oriented Programming classification trees, What Is a Decision Tree?


pages: 219 words: 65,532

The Numbers Game: The Commonsense Guide to Understanding Numbers in the News,in Politics, and inLife by Michael Blastland, Andrew Dilnot

Atul Gawande, business climate, correlation does not imply causation, credit crunch, happiness index / gross national happiness, Intergovernmental Panel on Climate Change (IPCC), moral panic, pension reform, pensions crisis, randomized controlled trial, school choice, very high income

Nowadays these are referred to as “adverse events,” making it clear that the cause was unclear and they might have had nothing to do with the medication. Restlessness for the true cause is a constructive habit, an insurance against gullibility. And though correlation does not prove causation, it is often a good hint, but a hint to start asking questions, not to settle for easy answers. There is one caveat. Here and there you will come across a tendency to dismiss almost all statistical findings as correlation-causation fallacy, a rhetorical cudgel, as one careful critic put it, to avoid believing any evidence. But we need to distinguish between casual associations often made for political ends and proper statistical studies.

From applying it all the time, people acquire a headstrong tendency to see it everywhere, even where it isn’t. We see how one thing goes with another—and quickly conclude that it causes the other, and never more so than when the numbers or measurements seem to agree. This is the oldest fallacy in the book, that correlation proves causation, and also the most obdurate. And so it has been observed by smart researchers that overweight people live longer than thinner people, and therefore it was concluded that being overweight causes longer life. Does it? We will see. How do we train the instinct that serves us so well most of the time for the occasions when it doesn’t?

There are many possible causes of acne, even in lovers of heavy metal, the likelier culprits being teenage hormones and diet. Correlation—the apparent link between two separate things—does not prove causation: just because two things seem to go together doesn’t mean one brings about the other. This shouldn’t need saying, but it does, hourly. Get this wrong—mistake correlation for causation—and we flout one of the most elementary rules of statistics or logic. When we spot a fallacy of this kind lurking behind a claim, we cannot believe anyone could have fallen for it. That is, until tomorrow, when we miss precisely the same kind of fallacy and then see fit to say the claim is supported by compelling evidence.


pages: 475 words: 134,707

The Hype Machine: How Social Media Disrupts Our Elections, Our Economy, and Our Health--And How We Must Adapt by Sinan Aral

Airbnb, Albert Einstein, algorithmic bias, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, Bernie Sanders, Big Tech, bitcoin, carbon footprint, Cass Sunstein, computer vision, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, cryptocurrency, data science, death of newspapers, digital nomad, disinformation, disintermediation, Donald Trump, Drosophila, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, experimental subject, facts on the ground, Filter Bubble, George Floyd, global pandemic, hive mind, illegal immigration, income inequality, Kickstarter, knowledge worker, longitudinal study, low skilled workers, Lyft, Mahatma Gandhi, Mark Zuckerberg, Menlo Park, meta-analysis, Metcalfe’s law, mobile money, move fast and break things, move fast and break things, multi-sided market, Nate Silver, natural language processing, Network effects, performance metric, phenotype, recommendation engine, Robert Bork, Robert Shiller, Second Machine Age, sentiment analysis, shareholder value, Sheryl Sandberg, skunkworks, Snapchat, social graph, social intelligence, social software, social web, statistical model, stem cell, Stephen Hawking, Steve Bannon, Steve Jobs, Steve Jurvetson, surveillance capitalism, Telecommunications Act of 1996, The Chicago School, the strength of weak ties, The Wisdom of Crowds, theory of mind, Tim Cook: Apple, Uber and Lyft, uber lyft, WikiLeaks, Yogi Berra

We must embrace causal lift. Taking Causality Seriously I have an xkcd cartoon about the difference between correlation and causation on the door to my office at MIT. It depicts two friends talking. One friend says to the other, “I used to think correlation implied causation. Then I took a statistics class [and] now I don’t.” The other friend says, “Sounds like the class helped,” and the first friend replies, “Well, maybe.” It could be that the class taught the friend about the difference between correlation and causation. It could also just as easily be that the friend who took the class has an interest in and thus a proclivity to understand statistics.

It could also just as easily be that the friend who took the class has an interest in and thus a proclivity to understand statistics. So maybe he “selected into” the class. This “selection effect” can explain the correlation between taking the class and understanding the difference between correlation and causation just as easily as the class teaching him about this difference. This selection effect is a serious problem for measuring the return on hype. Why? Because social media messages are targeted at people who are likely to be susceptible to them. Brands pay consultants big bucks to target their ads at the people most likely to buy their products. Targeting increases conversion rates even without changing anyone’s behavior, because it selects the most likely purchasers to receive ads.

The area under the ROC curve represents model performance. The greater the area under a model’s curve and above the 45-degree dotted line, the better the model performs. When Thomas Blake, Chris Nosko, and Steven Tadelis compared the ROI measures eBay was using to experimental measures that distinguished correlation from causation, they found brand search ad effectiveness was overestimated at eBay by up to 4,100 percent. Comparing traditional measures to a large experiment measuring the returns on Web display ads on Yahoo!, Randall Lewis and David Reiley found ROI inflation of 300 percent. In a large-scale experiment testing the effectiveness of retargeting ads compared to industry studies, Garrett Johnson, Randall Lewis, and Elmar Nubbemeyer found overestimates up to 1,600 percent.


Calling Bullshit: The Art of Scepticism in a Data-Driven World by Jevin D. West, Carl T. Bergstrom

airport security, algorithmic bias, Amazon Mechanical Turk, Andrew Wiles, bitcoin, Charles Babbage, cloud computing, computer vision, content marketing, correlation coefficient, correlation does not imply causation, crowdsourcing, cryptocurrency, data science, delayed gratification, disinformation, Dmitri Mendeleev, Donald Trump, Elon Musk, epigenetics, Estimating the Reproducibility of Psychological Science, experimental economics, Goodhart's law, Helicobacter pylori, invention of the printing press, John Markoff, longitudinal study, Lyft, meta-analysis, new economy, opioid epidemic / opioid crisis, p-value, Pluto: dwarf planet, publication bias, RAND corporation, randomized controlled trial, replication crisis, ride hailing / ride sharing, Ronald Reagan, selection bias, self-driving car, Silicon Valley, Silicon Valley startup, social graph, Socratic dialogue, Stanford marshmallow experiment, statistical model, stem cell, superintelligent machines, tech bro, the scientific method, theory of mind, Tim Cook: Apple, twin studies, Uber and Lyft, Uber for X, uber lyft, When a measure becomes a target

While this headline doesn’t use the word “cause,” it does use the word “effect”—another way of suggesting causal relationships. Correlation doesn’t imply causation—but apparently it doesn’t sell newspapers either. If we have evidence of correlation but not causation, we shouldn’t be making prescriptive claims. NPR reporter Scott Horsley posted a tweet announcing that “Washington Post poll finds NPR listeners are among the least likely to fall for politicians’ false claims.” Fair enough. But this poll demonstrated only correlation, not causation. Yet Horsley’s tweet also recommended, “Inoculate yourself against B.S. Listen to NPR.” The problem with this logic is easy to spot.

Scientists have a number of techniques for measuring correlations and drawing inferences about causality from these correlations. But doing so is a tricky and sometimes contentious business, and these techniques are not always used as carefully as they ought to be. Moreover, when we read about recent studies in medicine or policy or any other area, these subtleties are often lost. It is a truism that correlation does not imply causation. Do not leap carelessly from data showing the former to assumptions about the latter.*4 This is difficult to avoid, because people use data to tell stories. The stories that draw us in show a clear connection between cause and effect. Unfortunately, one of the most frequent misuses of data, particularly in the popular press, is to suggest a cause-and-effect relationship based on correlation alone.

There are many other confounding factors that could explain this relationship as well, such as the possibility that cultural values or the cost of child care varies across counties with some correlation to home values. So far, no bullshit. This is the right way to report the study’s findings. The Zillow article describes a correlation, and then uses this correlation to generate hypotheses about causation but does not leap to unwarranted conclusions about causality. Given that the study looks only at women aged 25 to 29, we might suspect that women with characteristics that make them likely to delay starting a family are also prone to moving to cities with high housing costs.


pages: 227 words: 62,177

Numbers Rule Your World: The Hidden Influence of Probability and Statistics on Everything You Do by Kaiser Fung

Alan Greenspan, American Society of Civil Engineers: Report Card, Andrew Wiles, Bernie Madoff, Black Swan, business cycle, call centre, correlation does not imply causation, cross-subsidies, Daniel Kahneman / Amos Tversky, edge city, Emanuel Derman, facts on the ground, financial engineering, fixed income, Gary Taubes, John Snow's cholera map, moral hazard, p-value, pattern recognition, profit motive, Report Card for America’s Infrastructure, statistical model, the scientific method, traveling salesman

Box’s writings on his experiences in the industry have inspired generations of statisticians; to get a flavor of his engaging style, see the collection Improving Almost Anything, lovingly produced by his former students. More ink than necessary has been spilled on the dichotomy between correlation and causation. Asking for the umpteenth time whether correlation implies causation is pointless (we already know it does not). The question Can correlation be useful without causation? is much more worthy of exploration. Forgetting what the textbooks say, most practitioners believe the answer is quite often yes. In the case of credit scoring, correlation-based statistical models have been wildly successful even though they do not yield simple explanations for why one customer is a worse credit risk than another.

It is implausible that something as variable as human behavior can be attributed to simple causes; modelers specializing in stock market investment and consumer behavior have also learned similar lessons. Statisticians in these fields have instead relied on accumulated learning from the past. The standard statistics book grinds to a halt when it comes to the topic of correlation versus causation. As readers, we may feel as if the authors have taken us along for the ride! After having plodded through the mathematics of regression modeling, we reach a section that screams, “Correlation is not causation!” and, “Beware of spurious correlations!” over and over. The bottom line, the writers tell us, is that almost nothing we have studied can prove causation; their motley techniques measure only correlation.

As interesting as it would be to know how each step of a touring plan decreased their wait times, Testa’s millions of fans care about only one thing: whether the plan let them visit more rides, enhancing the value of their entry tickets. The legion of satisfied readers is testimony to the usefulness of this correlational model. ~###~ Polygraphs rely strictly on correlations between the act of lying and certain physiological metrics. Are correlations useful without causation? In this case, statisticians say no. To avoid falsely imprisoning innocent people based solely on evidence of correlation, they insist that lie detection technology adopt causal modeling of the type practiced in epidemiology. They caution against logical overreach: Liars breathe faster.


pages: 397 words: 109,631

Mindware: Tools for Smart Thinking by Richard E. Nisbett

affirmative action, Albert Einstein, availability heuristic, big-box store, Cass Sunstein, choice architecture, cognitive dissonance, correlation coefficient, correlation does not imply causation, cosmological constant, Daniel Kahneman / Amos Tversky, dark matter, Edward Jenner, endowment effect, experimental subject, feminist movement, fixed income, fundamental attribution error, Garrett Hardin, glass ceiling, Henri Poincaré, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, job satisfaction, Kickstarter, lake wobegon effect, libertarian paternalism, longitudinal study, loss aversion, low skilled workers, Menlo Park, meta-analysis, quantitative easing, Richard Thaler, Ronald Reagan, selection bias, Shai Danziger, Socratic dialogue, Steve Jobs, Steven Levy, tacit knowledge, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Tragedy of the Commons, William of Occam, Zipcar

Many of my fellow psychologists are going to be distressed by my bottom line here: such questions as whether academic success is affected by self-esteem, controlling for depression, or whether the popularity of fraternity brothers is affected by extroversion, controlling for neuroticism, or whether the number of hugs a person receives per day confers resistance to infection, controlling for age, educational attainment, frequency of social interaction, and a dozen other variables, are not answerable by MRA. What nature hath joined together, multiple regression analysis cannot put asunder. No Correlation Doesn’t Mean No Causation Correlation doesn’t prove causation. But the problem with correlational studies is worse than that. Lack of correlation doesn’t prove lack of causation—and this mistake is made possibly as often as the converse error. Does diversity training improve rates of hiring women and minorities? One study examined this question by quizzing human resource managers at seven hundred U.S. organizations about whether they had diversity training programs and by checking on the firms’ minority hiring rates filed with the Equal Employment Opportunity Commission.31 As it happens, having diversity training programs was unrelated to “the share of white women, black women, and black men in management.”

The availability heuristic can also play a role. If the occasions when A is associated with B are more memorable than occasions when it isn’t, we’re particularly likely to overestimate the strength of the relationship. Correlation doesn’t establish causation, but if there’s a plausible reason why A might cause B, we readily assume that correlation does indeed establish causation. A correlation between A and B could be due to A causing B, B causing A, or something else causing both. We too often fail to consider these possibilities. Part of the problem here is that we don’t recognize how easy it is to “explain” correlations in causal terms.

This is usually not the case, at least for behavioral data. Self-esteem and depression are intrinsically bound up with each other. It’s entirely artificial to ask whether one of those variables has an effect on a dependent variable independent of the effects of the other variable. Just as correlation doesn’t prove causation, absence of correlation fails to prove absence of causation. False-negative findings can occur using MRA just as false-positive findings do—because of the hidden web of causation that we’ve failed to identify. 12. Don’t Ask, Can’t Tell How many questionnaire and survey results about people’s beliefs, values, or behavior will you read during your lifetime in newspapers, magazines, and business reports?


pages: 267 words: 71,123

End This Depression Now! by Paul Krugman

airline deregulation, Alan Greenspan, Asian financial crisis, asset-backed security, bank run, banking crisis, bond market vigilante , Bretton Woods, business cycle, capital asset pricing model, Carmen Reinhart, centre right, correlation does not imply causation, credit crunch, Credit Default Swap, currency manipulation / currency intervention, debt deflation, Eugene Fama: efficient market hypothesis, financial deregulation, financial innovation, Financial Instability Hypothesis, full employment, German hyperinflation, Gordon Gekko, high-speed rail, Hyman Minsky, income inequality, inflation targeting, invisible hand, It's morning again in America, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", Joseph Schumpeter, junk bonds, Kenneth Rogoff, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, Long Term Capital Management, low skilled workers, Mark Zuckerberg, Money creation, money market fund, moral hazard, mortgage debt, negative equity, paradox of thrift, Paul Samuelson, price stability, quantitative easing, rent-seeking, Robert Gordon, Ronald Reagan, salary depends on his not understanding it, Savings and loan crisis, Upton Sinclair, We are the 99%, working poor, Works Progress Administration

Invariably there would be questions about whether that meant that we were on the verge of another Great Depression—and I would declare that this wasn’t necessarily so, that there was no reason extreme inequality would necessarily cause economic disaster. Well, whaddya know? Still, correlation is not the same as causation. The fact that a return to pre-Depression levels of inequality was followed by a return to depression economics could be just a coincidence. Or it could reflect common causes of both phenomena. What do we really know here, and what might we suspect? Common causation is almost surely part of the story.

The Trouble with Correlation You might think that the way to assess the effects of government spending on the economy is simply to look at the correlation between spending levels and other things, like growth and employment. The truth is that even people who should know better sometimes fall into the trap of equating correlation with causation (see the discussion of debt and growth in chapter 8). But let me try to disabuse you of the notion that this is a useful procedure, by talking about a related question: the effects of tax rates on economic performance. As you surely know, it’s an article of faith on the American right that low taxes are the key to economic success.

., 200 conservatives: anti-government ideology of, 66 anti-Keynesianism of, 93–96, 106–8, 110–11 Big Lie of 2008 financial crisis espoused by, 64–66, 100 free market ideology of, 66 Consumer Financial Protection Bureau, 84 Consumer Price Index (CPI), 156–57, 159, 160 consumer spending, 24, 26, 30, 32, 33, 39, 41, 113, 136 effect of government spending on, 39 household debt and, 45, 47, 126, 146 income inequality and, 83 in 2008 financial crisis, 117 conventional wisdom, lessons of Great Depression ignored in, xi corporations, 30 see also business investment, slump in; executive compensation correlation, causation vs., 83, 198, 232–33, 237 Cowen, Brian, 88 credit booms, 65 credit crunches: of 2008, 41, 110, 113, 117 Great Depression and, 110 credit default swaps, 54, 55 credit expansion, 154 currency, manipulation of, 221 currency, national: devaluation of, 169 disadvantages of, 168–69, 170–71 flexibility of, 169–73, 179 optimum currency area and, 171–72 see also euro Dakotas, high employment in, 37 debt, 4, 34, 131 deregulation and, 50 high levels of, 34, 45, 46, 49–50, 51 self-reinforcing downward spiral in, 46, 48, 49–50 usefulness of, 43 see also deficits; government debt; household debt; private debt “Debt-Deflation Theory of Great Depressions, The” (Fisher), 45 debt relief, 147 defense industry, 236 defense spending, 35, 38–39, 148, 234–35, 235, 236 deficits, 130–49, 151, 202, 238 Alesina/Ardagna study of, 196–99 depressions and, 135–36, 137 exaggerated fear of, 131–32, 212 job creation vs., 131, 143, 149, 206–7, 238 monetary policy and, 135 see also debt deflation, 152, 188 debt and, 45, 49, 163 De Grauwe, Paul, 182–83 deleveraging, 41, 147 paradox of, 45–46, 52 demand, 24–34 in babysitting co-op example, 29–30 inadequate levels of, 25, 29–30, 34, 38, 47, 93, 101–2, 118, 136, 148 spending and, 24–26, 29, 47, 118 unemployment and, 33, 47 see also supply and demand Democracy Corps, 8 Democrats, Democratic Party, 2012 election and, 226, 227–28 Denmark, 184 EEC joined by, 167 depression of 2008–, ix–xii, 209–11 business investment and, 16, 33 debt levels and, 4, 34, 47 democratic values at risk in, 19 economists’ role in, 100–101, 108 education and, 16 in Europe, see Europe, debt crisis in housing sector and, 33, 47 income inequality and, 85, 89–90 inflation rate in, 151–52, 156–57, 159–61, 189, 227 infrastructure investment and, 16–17 lack of demand in, 47 liquidity trap in, 32–34, 38, 51, 136, 155, 163 long-term effects of, 15–17 manufacturing capacity loss in, 16 as morality play, 23, 207, 219 private sector spending and, 33, 47, 211–12 unemployment in, x, 5–12, 24, 110, 117, 119, 210, 212 see also financial crisis of 2008–09; recovery, from depression of 2008– depressions, 27 disproportion between cause and effect in, 22–23, 30–31 government spending and, 135–36, 137, 231 Keynes’s definition of, x Schumpeter on, 204–5 see also Great Depression; recessions deregulation, financial, 54, 56, 67, 85, 114 under Carter, 61 under Clinton, 62 income inequality and, 72–75, 74, 81, 82, 89 under Reagan, 50, 60–61, 62, 67–68 rightward political shift and, 83 supposed benefits of, 69–70, 72–73, 86 derivatives, 98 see also specific financial instruments devaluation, 169, 180–81 disinflation, 159 dot-com bubble, 14, 198 Draghi, Mario, 186 earned-income tax credit, 120 econometrics, 233 economic output, see gross domestic product Economics (Samuelson), 93 economics, economists: academic sociology and, 92, 96, 103 Austrian school of, 151 complacency of, 55 disproportion between cause and effect in, 22–23, 30–31 ignorance of, 106–8 influence of financial elite on, 96 Keynesian, see Keynesian economics laissez-faire, 94, 101 lessons of Great Depression ignored by, xi, 92, 108 liquidationist school of, 204–5 monetarist, 101 as morality play, 23, 207, 219 renewed appreciation of past thinking in, 42 research in, see research, economic Ricardian, 205–6 see also macroeconomics “Economics of Happiness, The” (Bernanke), 5 economy, U.S.: effect of austerity programs on, 51, 213 election outcomes and, 225–26 postwar boom in, 50, 70, 149 size of, 121, 122 supposed structural defects in, 35–36 see also global economy education: austerity policies and, 143, 213–14 depression of 2008– and, 16 income inequality and, 75–76, 89 inequality in, 84 teachers’ salaries in, 72, 76, 148 efficient-markets hypothesis, 97–99, 100, 101, 103–4 Eggertsson, Gauti, 52 Eichengreen, Barry, 236 elections, U.S.: economic growth and, 225–26 of 2012, 226 emergency aid, 119–20, 120, 144, 216 environmental regulation, 221 Essays in Positive Economics (Friedman), 170 euro, 166 benefits of, 168–69, 170–71 creation of, 174 economic flexibility constrained by, 18, 169–73, 179, 184 fixing problems of, 184–87 investor confidence and, 174 liquidity and, 182–84, 185 trade imbalances and, 175, 175 as vulnerable to panics, 182–84, 186 wages and, 174–75 Europe: capital flow in, 169, 174, 180 common currency of, see euro creditor nations of, 46 debtor nations of, 4, 45, 46, 139 democracy and unity in, 184–85 fiscal integration lacking in, 171, 172–73, 176, 179 GDP in, 17 health care in, 18 inflation and, 185, 186 labor mobility lacking in, 171–72, 173, 179 1930s arms race in, 236 social safety nets in, 18 unemployment in, 4, 17, 18, 176, 229, 236 Europe, debt crisis in, x, 4, 40, 45, 46, 138, 140–41, 166–87 austerity programs in, 46, 144, 185, 186, 188, 190 budget deficits and, 177 fiscal irresponsibility as supposed cause of (Big Delusion), 177–79, 187 housing bubbles and, 65, 169, 172, 174, 176 interest rates in, 174, 176, 182–84, 190 liquidity fears and, 182–84 recovery from, 184–87 unequal impact of, 17–18 wages in, 164–65, 169–70, 174–75 European Central Bank, 46, 183 Big Delusion and, 179 inflation and, 161, 180 interest rates and, 190, 202–3 monetary policy of, 180, 185, 186 European Coal and Steel Community, 167 European Economic Community (EEC), 167–68 European Union, 172 exchange rates, fixed vs. flexible, 169–73 executive compensation, 78–79 “outrage constraint” on, 81–82, 83 expansionary austerity, 144, 196–99 expenditure cascades, 84 Fama, Eugene, 69–70, 73, 97, 100, 106 Fannie Mae, 64, 65–66, 100, 172, 220–21 Farrell, Henry, 100, 192 Federal Deposit Insurance Corporation (FDIC), 59, 172 Federal Housing Finance Agency, 221 Federal Reserve, 42, 103 aggressive action needed from, 216–19 creation of, 59 foreign exchange intervention and, 217 inflation and, 161, 217, 219, 227 interest rates and, 33–34, 93, 105, 117, 134, 135, 143, 151, 189–90, 193, 215, 216–17 as lender of last resort, 59 LTCM crisis and, 69 money supply controlled by, 31, 32, 33, 105, 151, 153, 155, 157, 183 recessions and, 105 recovery and, 216–19 in 2008 financial crisis, 104, 106, 116 unconventional asset purchases by, 217 Federal Reserve Bank of Boston, 47–48 Feinberg, Larry, 72 Ferguson, Niall, 135–36, 139, 160 Fianna Fáil, 88 filibusters, 123 financial crisis of 2008–09, ix, x, 40, 41, 69, 72, 99, 104, 111–16 Bernanke on, 3–4 Big Lie of, 64–66, 100, 177 capital ratios and, 59 credit crunch in, 41, 110, 113, 117 deleveraging in, 147 Federal Reserve and, 104, 106 income inequality and, 82, 83 leverage in, 44–46, 63 panics in, 4, 63, 111, 155 real GDP in, 13 see also depression of 2008–; Europe, debt crisis in financial elite: political influence of, 63, 77–78, 85–90 Republican ideology and, 88–89 top 0.01 percent in, 75, 76 top 0.1 percent in, 75, 76, 77, 96 top 1 percent in, 74–75, 74, 76–77, 96 see also income inequality financial industry, see banks, banking industry financial instability hypothesis, 43–44 Financial Times, 95, 100, 203–4 Finland, 184 fiscal integration, 171, 172–73, 176 Fisher, Irving, 22, 42, 44–46, 48, 49, 52, 163 flexibility: currency and, 18, 169–73 paradox of, 52–53 Flip This House (TV show), 112 Florida, 111 food stamps, 120, 144 Ford, John, 56 foreclosures, 45, 127–28 foreign exchange markets, 217 foreign trade, 221 Fox News, 134 Frank, Robert, 84 Freddie Mac, 64, 65–66, 100, 172, 220–21 free trade, 167 Friedman, Milton, 96, 101, 181, 205 on causes of Great Depression, 105–6 Gabriel, Peter, 20 Gagnon, Joseph, 219, 221 Gardiner, Chance (char.), 3 Garn–St.


pages: 147 words: 39,910

The Great Mental Models: General Thinking Concepts by Shane Parrish

Albert Einstein, Atul Gawande, Barry Marshall: ulcers, bitcoin, Black Swan, colonial rule, correlation coefficient, correlation does not imply causation, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, delayed gratification, feminist movement, Garrett Hardin, index fund, Isaac Newton, Jane Jacobs, John Bogle, mandelbrot fractal, Pierre-Simon Laplace, Ponzi scheme, Richard Feynman, statistical model, stem cell, The Death and Life of Great American Cities, the map is not the territory, the scientific method, Thomas Bayes, Torches of Freedom, Tragedy of the Commons

It is entirely possible that having parents who consume a lot of alcohol leads to worse academic outcomes for their children. It is also possible, however, that the reverse is true, or even that having kids who do poorly in school causes parents to drink more. Trying to invert the relationship can help you sort through claims to determine if you are dealing with true causation or just correlation. Causation Whenever correlation is imperfect, extremes will soften over time. The best will always appear to get worse and the worst will appear to get better, regardless of any additional action. This is called regression to the mean, and it means we have to be extra careful when diagnosing causation.


Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth by Stuart Ritchie

Albert Einstein, anesthesia awareness, Bayesian statistics, Carmen Reinhart, Cass Sunstein, Charles Babbage, citation needed, Climatic Research Unit, cognitive dissonance, complexity theory, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, data science, deindustrialization, Donald Trump, double helix, en.wikipedia.org, epigenetics, Estimating the Reproducibility of Psychological Science, Goodhart's law, Growth in a Time of Debt, Helicobacter pylori, Kenneth Rogoff, l'esprit de l'escalier, meta-analysis, microbiome, Milgram experiment, mouse model, New Journalism, p-value, phenotype, placebo effect, profit motive, publication bias, publish or perish, race to the bottom, randomized controlled trial, recommendation engine, rent-seeking, replication crisis, Richard Thaler, risk tolerance, Ronald Reagan, Scientific racism, selection bias, Silicon Valley, Silicon Valley startup, Stanford prison experiment, statistical model, stem cell, Steven Pinker, Thomas Bayes, twin studies, Tyler Cowen, University of East Anglia, Wayback Machine

You often see a slightly different version: ‘correlation does not imply causation’. There’s an ambiguity here due to the multiple meanings of the word ‘imply’. In its strong definition (thing A logically involves thing B, in the same way that the existence of, say, a dance implies that there’s a dancer), it’s certainly true. But in its weak version (thing A suggests thing B without explicitly saying thing B, in the same way that a receiving a slightly terse email from your boss might imply that they’re unhappy with you), then it’s not correct. In that weak sense, a correlation does sometimes imply causation, even if there’s no causation there at all.

The psychophysiologist James Heathers has set up a novelty Twitter account that exists solely to retweet misleading news headlines from translational studies, such as ‘Scientists Develop Jab that Stops Craving for Junk Food’ or ‘Compounds in Carrots Reverse Alzheimer’s-Like Symptoms’ with a simple but accurate addition: ‘… IN MICE’.21 The third kind of hype found by the Cardiff team was possibly the most embarrassing. Everyone, especially scientists, is supposed to know that correlation is not causation.22 This basic insight is taught in every elementary statistics course and is a perennial feature of public debates about science, education, economics and more. When scientists look at an observational dataset, where data have been gathered without any randomised experimental intervention – say, a study charting the growth in children’s vocabulary as they get older – they’re generally just looking at correlations.

The statistician Matthew Hankins has amassed a collection of genuine quotes from published papers where p-values remained stubbornly above that threshold, but whose authors clearly had a strong desire for significant results: • ‘a trend that approached significance’ (for a result reported as ‘p < 0.06’) • ‘fairly significant’ (p = 0.09) • ‘significantly significant’ (p = 0.065) • ‘narrowly eluded statistical significance’ (p = 0.0789) • ‘hovered around significance’ (p = 0.061) • ‘very closely brushed the limit of statistical significance’ (p = 0.051) • ‘not absolutely significant but very probably so’ (p > 0.05)62 There’s a whole literature of studies by scientific spin-watchers, each of them highlighting spin in their own fields. 15 per cent of trials in obstetrics and gynaecology spun their non-significant results as if they showed benefits of the treatment.63 35 per cent of studies of prognostic tests for cancer used spin to obfuscate the non-significant nature of their findings.64 47 per cent of trials of obesity treatments published in top journals were spun in some way.65 83 per cent of papers reporting trials of antidepressant and anxiety medication failed to discuss important limitations of their study design.66 A review of brain-imaging studies concluded that hyping up correlation into causation was ‘rampant’.67 Some forms of spin shade into fraud, or at least gross incompetence: a 2009 review showed that, of a sample of studies published in Chinese medical journals that claimed to be randomised controlled trials, only 7 per cent actually used randomisation.68 Even meta-analyses aren’t safe, as we’ve seen before.


pages: 624 words: 127,987

The Personal MBA: A World-Class Business Education in a Single Volume by Josh Kaufman

Albert Einstein, Alvin Toffler, Atul Gawande, Black Swan, business cycle, business process, buy low sell high, capital asset pricing model, Checklist Manifesto, cognitive bias, correlation does not imply causation, Credit Default Swap, Daniel Kahneman / Amos Tversky, David Heinemeier Hansson, David Ricardo: comparative advantage, Dean Kamen, delayed gratification, discounted cash flows, Donald Knuth, double entry bookkeeping, Douglas Hofstadter, en.wikipedia.org, Frederick Winslow Taylor, George Santayana, Gödel, Escher, Bach, high net worth, hindsight bias, index card, inventory management, iterative process, job satisfaction, Johann Wolfgang von Goethe, Kevin Kelly, Kickstarter, Lao Tzu, lateral thinking, loose coupling, loss aversion, Marc Andreessen, market bubble, Network effects, Parkinson's law, Paul Buchheit, Paul Graham, place-making, premature optimization, Ralph Waldo Emerson, rent control, scientific management, side project, statistical model, stealth mode startup, Steve Jobs, Steve Wozniak, subscription business, telemarketer, the scientific method, time value of money, Toyota Production System, tulip mania, Upton Sinclair, Vilfredo Pareto, Walter Mischel, Y Combinator, Yogi Berra

Midranges are best used for quick estimates—they’re fast, and you only need to know two data points, but they can be easily skewed by outliers that are abnormally high or low, like Bill Gates’s bank balance. Means, Medians, Modes, and Midranges are useful analytical tools that can indicate typical results—provided you’re careful enough to use the right tool for the job. SHARE THIS CONCEPT: http://book.personalmba.com/mean-median-mode-midrange/ Correlation and Causation Correlation isn’t causation, but it sure is a hint. —EDWARD TUFTE, STATISTICIAN, INFORMATION DESIGN EXPERT, AND PROFESSOR AT YALE UNIVERSITY Imagine a billiards table: if you know the exact position of every ball on the table and the details of the forces applied to the cue ball (impact vector, impact force, location of impact, table friction, and air resistance), you can calculate exactly how the cue ball will travel and how it will affect other balls it hits along the way.

Here’s another thought experiment, using hypothetical data: people who suffer heart attacks eat, on average, 57 bacon double cheeseburgers every year. Does eating bacon double cheeseburgers cause heart attacks? Not necessarily. People who suffer heart attacks typically take 365 showers a year and blink their eyes 5.6 million times a year. Do taking showers and blinking your eyes cause heart attacks as well? Correlation is not Causation. Even if you notice that one measurement is highly associated with another, that does not prove that one thing caused the other. Imagine you own a pizza parlor, and you create a thirty-second advertisement to air on local television. Shortly after the commercial goes live, you notice a 30 percent increase in sales.

For example, if you know that families go out to celebrate the end of school or that an annual convention is coming up, you can adjust for that seasonality by using historical data. The more you can isolate the change you made in the system from other factors, the more confidence you can have that the change you made intentionally actually caused the results you see. SHARE THIS CONCEPT: http://book.personalmba.com/correlation-causation/ Norms Those who cannot remember the past are condemned to repeat it. —GEORGE SANTAYANA, PHILOSOPHER, ESSAYIST, AND APHORIST If you want to compare the effectiveness of something in the present, it’s often useful to learn from the past. Norms are measures that use historical data as a tool to provide Context for current Measurements.


pages: 347 words: 99,969

Through the Language Glass: Why the World Looks Different in Other Languages by Guy Deutscher

Alfred Russel Wallace, correlation does not imply causation, Kickstarter, offshore financial centre, pattern recognition, Ralph Waldo Emerson, Sapir-Whorf hypothesis, Silicon Valley, Steven Pinker

In his room the bed was in the north, in yours it is in the south; the telephone that in his room was in the west is now in the east. So while you will see and remember the same room twice, the Guugu Yimithirr speaker will see and remember two different rooms. CORRELATION OR CAUSATION? One of the most tempting and most common of all logical fallacies is to jump from correlation to causation: to assume that just because two facts correlate, one of them was the cause of the other. To reduce this kind of logic ad absurdum, I could advance the brilliant new theory that language can affect your hair color. In particular, I claim that speaking Swedish makes your hair go blond and speaking Italian makes your hair go dark.

So the only imaginable mechanism that could provide such intense drilling in orientation at such a young age is the spoken language—the need to know the directions in order to be able to communicate about the simplest aspects of everyday life. There is thus a compelling case that the relation between language and spatial thinking is not just correlation but causation, and that one’s mother tongue affects how one thinks about space. In particular, a language like Guugu Yimithirr, which forces its speakers to use geographic coordinates at all times, must be a crucial factor in bringing about the perfect pitch for directions and the corresponding patterns of memory that seem so weird and unattainable to us.

And while this is as much as we can say with absolute certainty, it is plausible to go one step further and make the following inference: since people tend to react more quickly to color recognition tasks the farther apart the two colors appear to them, and since Russians react more quickly to shades across the siniy-goluboy border than what the objective distance between the hues would imply, it is plausible to conclude that neighboring hues around the border actually appear farther apart to Russian speakers than they are in objective terms. Of course, even if differences between the behavior of Russian and English speakers have been demonstrated objectively, it is always dangerous to jump automatically from correlation to causation. How can we be sure that the Russian language in particular—rather than anything else in the Russians’ background and upbringing—had any causal role in producing their response to colors near the border? Maybe the real cause of their quicker reaction time lies in the habit of Russians to spend hours on end gazing intently at the vast expanses of Russian sky?


pages: 337 words: 86,320

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz

affirmative action, AltaVista, Amazon Mechanical Turk, Asian financial crisis, Bernie Sanders, big data - Walmart - Pop Tarts, Cass Sunstein, computer vision, content marketing, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, desegregation, Donald Trump, Edward Glaeser, Filter Bubble, game design, happiness index / gross national happiness, income inequality, Jeff Bezos, John Snow's cholera map, longitudinal study, Mark Zuckerberg, Nate Silver, peer-to-peer lending, Peter Thiel, price discrimination, quantitative hedge fund, Ronald Reagan, Rosa Parks, sentiment analysis, Silicon Valley, statistical model, Steve Jobs, Steven Levy, Steven Pinker, TaskRabbit, The Signal and the Noise by Nate Silver, working poor

And the last minute of this game will do something that, for an economist, is far more profound: the last sixty seconds will help finally tell us, once and for all, Do advertisements work? The notion that ads improve sales is obviously crucial to our economy. But it is maddeningly hard to prove. In fact, this is a textbook example of exactly how difficult it is to distinguish between correlation and causation. There’s no doubt that products that advertise the most also have the highest sales. Twentieth Century Fox spent $150 million marketing the movie Avatar, which became the highest-grossing film of all time. But how much of the $2.7 billion in Avatar ticket sales was due to the heavy marketing?

If we did this, we would find that students who went to Stuyvesant score much higher on standardized tests and get accepted to substantially better universities. But as we’ve seen already in this chapter, this kind of evidence, by itself, is not convincing. Maybe the reason Stuyvesant students perform so much better is that Stuy attracts much better students in the first place. Correlation here does not prove causation. To test the causal effects of Stuyvesant High School, we need to compare two groups that are almost identical: one that got the Stuy treatment and one that did not. We need a natural experiment. But where can we find it? The answer: students, like Yilmaz, who scored very, very close to the cutoff necessary to attend Stuyvesant.* Students who just missed the cutoff are the control group; students who just made the cut are the treatment group.

Does it matter if you go to one of the best universities in the world, such as Harvard, or a solid school such as Penn State? Once again, there is a clear correlation between the ranking of one’s school and how much money people make. Ten years into their careers, the average graduate of Harvard makes $123,000. The average graduate of Penn State makes $87,800. But this correlation does not imply causation. Two economists, Stacy Dale and Alan B. Krueger, thought of an ingenious way to test the causal role of elite universities on the future earning potential of their graduates. They had a large dataset that tracked a whole host of information on high school students, including where they applied to college, where they were accepted to college, where they attended college, their family background, and their income as adults.


pages: 829 words: 186,976

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't by Nate Silver

"Robert Solow", airport security, Alan Greenspan, Alvin Toffler, An Inconvenient Truth, availability heuristic, Bayesian statistics, Bear Stearns, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, big-box store, Black Monday: stock market crash in 1987, Black Swan, Boeing 747, Broken windows theory, business cycle, buy and hold, Carmen Reinhart, Charles Babbage, Claude Shannon: information theory, Climategate, Climatic Research Unit, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, computer age, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, Daniel Kahneman / Amos Tversky, disinformation, diversification, Donald Trump, Edmond Halley, Edward Lorenz: Chaos theory, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, everywhere but in the productivity statistics, fear of failure, Fellow of the Royal Society, Freestyle chess, fudge factor, Future Shock, George Akerlof, global pandemic, Goodhart's law, haute cuisine, Henri Poincaré, high batting average, housing crisis, income per capita, index fund, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, invention of the printing press, invisible hand, Isaac Newton, James Watt: steam engine, John Bogle, John Nash: game theory, John von Neumann, Kenneth Rogoff, knowledge economy, Laplace demon, locking in a profit, Loma Prieta earthquake, market bubble, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, Monroe Doctrine, mortgage debt, Nate Silver, negative equity, new economy, Norbert Wiener, PageRank, pattern recognition, pets.com, Phillips curve, Pierre-Simon Laplace, prediction markets, Productivity paradox, random walk, Richard Thaler, Robert Shiller, Rodney Brooks, Ronald Reagan, Saturday Night Live, savings glut, security theater, short selling, Skype, statistical model, Steven Pinker, The Great Moderation, The Market for Lemons, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, transfer pricing, University of East Anglia, Watson beat the top human players on Jeopardy!, Wayback Machine, wikimedia commons

These methods neither require nor encourage us to think about the plausibility of our hypothesis: the idea that cigarettes cause lung cancer competes on a level playing field with the idea that toads predict earthquakes. It is, I suppose, to Fisher’s credit that he recognized that correlation does not always imply causation. However, the Fisherian statistical methods do not encourage us to think about which correlations imply causations and which ones do not. It is perhaps no surprise that after a lifetime of thinking this way, Fisher lost the ability to tell the difference. Bob the Bayesian In the Bayesian worldview, prediction is the yardstick by which we measure progress.

First, it is very hard to determine cause and effect from economic statistics alone. Second, the economy is always changing, so explanations of economic behavior that hold in one business cycle may not apply to future ones. And third, as bad as their forecasts have been, the data that economists have to work with isn’t much good either. Correlations Without Causation The government produces data on literally 45,000 economic indicators each year.24 Private data providers track as many as four million statistics.25 The temptation that some economists succumb to is to put all this data into a blender and claim that the resulting gruel is haute cuisine.

Some studies have even claimed that the Leading Economic Index has no predictive power at all when applied in real time.35 “There’s very little that’s really predictive,” Hatzius told me. “Figuring out what’s truly causal and what’s correlation is very difficult to do.” Most of you will have heard the maxim “correlation does not imply causation.” Just because two variables have a statistical relationship with each other does not mean that one is responsible for the other. For instance, ice cream sales and forest fires are correlated because both occur more often in the summer heat. But there is no causation; you don’t light a patch of the Montana brush on fire when you buy a pint of Häagen-Dazs.


pages: 442 words: 94,734

The Art of Statistics: Learning From Data by David Spiegelhalter

algorithmic bias, Antoine Gombaud: Chevalier de Méré, Bayesian statistics, Brexit referendum, Carmen Reinhart, Charles Babbage, complexity theory, computer vision, correlation coefficient, correlation does not imply causation, dark matter, data science, Edmond Halley, Estimating the Reproducibility of Psychological Science, Gregor Mendel, Hans Rosling, Kenneth Rogoff, meta-analysis, Nate Silver, Netflix Prize, p-value, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, randomized controlled trial, recommendation engine, replication crisis, self-driving car, speech recognition, statistical model, The Design of Experiments, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus

And to give them credit, the authors of the paper doubted it too, adding, ‘Completeness of cancer registration and detection bias are potential explanations for the findings.’ In other words, wealthy people with higher education are more likely to be diagnosed and get their tumour registered, an example of what is known as ascertainment bias in epidemiology. ‘Correlation Does Not Imply Causation’ We saw in the last chapter how Pearson’s correlation coefficient measures how close the points on a scatter-plot are to a straight line. When considering English hospitals conducting children’s heart surgery in the 1990s, and plotting the number of cases against their survival, the high correlation showed that bigger hospitals were associated with lower mortality.

When considering English hospitals conducting children’s heart surgery in the 1990s, and plotting the number of cases against their survival, the high correlation showed that bigger hospitals were associated with lower mortality. But we could not conclude that bigger hospitals caused the lower mortality. This cautious attitude has a long pedigree. When Karl Pearson’s newly developed correlation coefficient was being discussed in the journal Nature in 1900, a commentator warned that ‘correlation does not imply causation’. In the succeeding century this phrase has been a mantra repeatedly uttered by statisticians when confronted by claims based on simply observing that two things tend to vary together. There is even a website that automatically generates idiotic associations, such as the delightful correlation of 0.96 between the annual per-capita consumption of mozzarella cheese in the US between 2000 and 2009, and the number of civil engineering doctorates awarded in each of those years.2 There seems to be a deep human need to explain things that happen in terms of simple cause–effect relationships – I am sure we could all construct a good story about all those new engineers gorging on pizzas.

But randomization is often difficult and sometimes impossible: we can’t test the effect of our habits by randomizing people to smoke or eat unhealthy diets (even though such experiments are performed on animals). When the data does not arise from an experiment, it is said to be observational. So often we are left with trying as best we can to sort out correlation from causation by using good design and statistical principles applied to observational data, combined with a healthy dose of scepticism. The issue of old men’s ears might be rather less important than some of the topics in this book, but illustrates the need for choosing study designs that are appropriate for answering questions.


pages: 404 words: 92,713

The Art of Statistics: How to Learn From Data by David Spiegelhalter

algorithmic bias, Antoine Gombaud: Chevalier de Méré, Bayesian statistics, Brexit referendum, Carmen Reinhart, Charles Babbage, complexity theory, computer vision, correlation coefficient, correlation does not imply causation, dark matter, data science, Edmond Halley, Estimating the Reproducibility of Psychological Science, Gregor Mendel, Hans Rosling, Kenneth Rogoff, meta-analysis, Nate Silver, Netflix Prize, p-value, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, randomized controlled trial, recommendation engine, replication crisis, self-driving car, speech recognition, statistical model, The Design of Experiments, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus

And to give them credit, the authors of the paper doubted it too, adding, ‘Completeness of cancer registration and detection bias are potential explanations for the findings.’ In other words, wealthy people with higher education are more likely to be diagnosed and get their tumour registered, an example of what is known as ascertainment bias in epidemiology. ‘Correlation Does Not Imply Causation’ We saw in the last chapter how Pearson’s correlation coefficient measures how close the points on a scatter-plot are to a straight line. When considering English hospitals conducting children’s heart surgery in the 1990s, and plotting the number of cases against their survival, the high correlation showed that bigger hospitals were associated with lower mortality.

When considering English hospitals conducting children’s heart surgery in the 1990s, and plotting the number of cases against their survival, the high correlation showed that bigger hospitals were associated with lower mortality. But we could not conclude that bigger hospitals caused the lower mortality. This cautious attitude has a long pedigree. When Karl Pearson’s newly developed correlation coefficient was being discussed in the journal Nature in 1900, a commentator warned that ‘correlation does not imply causation’. In the succeeding century this phrase has been a mantra repeatedly uttered by statisticians when confronted by claims based on simply observing that two things tend to vary together. There is even a website that automatically generates idiotic associations, such as the delightful correlation of 0.96 between the annual per-capita consumption of mozzarella cheese in the US between 2000 and 2009, and the number of civil engineering doctorates awarded in each of those years.2 There seems to be a deep human need to explain things that happen in terms of simple cause—effect relationships—I am sure we could all construct a good story about all those new engineers gorging on pizzas.

But randomization is often difficult and sometimes impossible: we can’t test the effect of our habits by randomizing people to smoke or eat unhealthy diets (even though such experiments are performed on animals). When the data does not arise from an experiment, it is said to be observational. So often we are left with trying as best we can to sort out correlation from causation by using good design and statistical principles applied to observational data, combined with a healthy dose of scepticism. The issue of old men’s ears might be rather less important than some of the topics in this book, but illustrates the need for choosing study designs that are appropriate for answering questions.


pages: 566 words: 153,259

The Panic Virus: The True Story Behind the Vaccine-Autism Controversy by Seth Mnookin

Albert Einstein, British Empire, Cass Sunstein, cognitive dissonance, correlation does not imply causation, Daniel Kahneman / Amos Tversky, disinformation, Edward Jenner, en.wikipedia.org, illegal immigration, index card, Isaac Newton, loss aversion, meta-analysis, mouse model, neurotypical, pattern recognition, placebo effect, precautionary principle, Richard Thaler, Saturday Night Live, selection bias, Solar eclipse in 1919, Stephen Hawking, Steven Pinker, the scientific method, Thomas Kuhn: the structure of scientific revolutions

Before continuing with the nationwide effort, his aides said, public health officials needed to promise that there would not be any more children who were diagnosed with polio after being vaccinated. That, as anyone with an elementary understanding of immunology knew, was an impossible guarantee to provide, and so, instead of trusting people to understand and accept that there are risks with every medical procedure and that correlation does not equal causation, or trying to explain that the problems appeared to be related to the specific conditions under which the infected batches had been produced and not with the safety of the vaccine generally, the government took the one step guaranteed to undermine public confidence: On May 7, Scheele announced that the polio vaccine program was being shut down so that the government, “with the help of the manufacturers,” could undertake “a reappraisal of all of their tests and procedures.”13 “The Public Health Service believes that every single step in the interest of safety must be taken,” he said.

Proving that the media’s frenzy for beating competitors by mere minutes is not a product of the Internet age, NBC immediately broke the embargo, and was just as quickly denounced by its competitors as forever tainting the sanctity of agreements made between reporters and their sources. 13 The difficulty in determining whether correlation equals causation causes an enormous number of misapprehensions. Until a specific mechanism demonstrating how A causes B is identified, it’s best to assume that any correlation is incidental, or that both A and B relate independently to some third factor. An example that highlights this is the correlation between drinking milk and cancer rates, which some advocacy groups (including People for the Ethical Treatment of Animals) use to argue that drinking milk causes cancer.

In this political battle, Kirby employed a time-honored tactic of push pollers and ward politicians: He used an ominoussounding claim—“Curiously, the first case of autism was not recorded until the early 1940s, a few years after thimerosal was introduced in vaccines”—to make his accusation sound as if it was idle speculation. In this case, Kirby both blurred the difference between correlation and causation and conflated the first time a disease is given a particular label with the first time it appears in a population. (It was a little like saying, “Curiously, schizophrenia was not identified as a disorder until the late 1880s, a few years after Alexander Graham Bell invented the telephone.”)


pages: 531 words: 125,069

The Coddling of the American Mind: How Good Intentions and Bad Ideas Are Setting Up a Generation for Failure by Greg Lukianoff, Jonathan Haidt

AltaVista, Bernie Sanders, bitcoin, Black Swan, cognitive dissonance, correlation does not imply causation, demographic transition, Donald Trump, Ferguson, Missouri, Filter Bubble, helicopter parent, Herbert Marcuse, hygiene hypothesis, income inequality, Internet Archive, Isaac Newton, low skilled workers, Mahatma Gandhi, mass immigration, mass incarceration, means of production, microaggression, moral panic, Nelson Mandela, Ralph Nader, risk tolerance, Silicon Valley, Snapchat, Steven Pinker, The Bell Curve by Richard Herrnstein and Charles Murray, Unsafe at Any Speed, Wayback Machine

When you see a situation in which some groups are underrepresented, it is an invitation to investigate and find out whether there are obstacles, a hostile climate, or systemic factors that have a disparate impact on members of those groups. But how can you know whether unequal outcomes truly reveal a violation of justice? Correlation Does Not Imply Causation All social scientists know that correlation does not imply causation. If A and B seem to be linked—that is, they change together over time or are found together in a population at levels higher than chance would predict—then it is certainly possible that A caused B. But it’s also possible that B caused A (reverse causation) or that a third variable, C, caused both A and B and there is no direct relationship between A and B.

An article about the study that was published at Gawker.com featured this headline: MORE BUCK FOR YOUR BANG: PEOPLE WHO HAVE MORE SEX MAKE THE MOST MONEY.38 The headline suggested that A (sex) causes B (money), which is surely the best causal path to choose if your goal is to entice people to click on your article. But any social scientist presented with that correlation would instantly wonder about reverse causation (does having more money cause people to have more sex?) and would then move on to a third-variable explanation, which in this case seems to be the correct one.39 The Gawker story itself noted that people who are more extraverted have more sex and also make more money. In this case, a third variable, C (extraversion, or high sociability) may cause both A (more sex) and B (more money).

In this case, a third variable, C (extraversion, or high sociability) may cause both A (more sex) and B (more money). Social scientists analyze correlations like this constantly (to the great annoyance of friends and family). They are self-appointed conversation referees, throwing a yellow penalty flag when anyone tries to interpret a correlation as evidence of causation. But a funny thing has been happening in recent years on campus. Nowadays, when someone points to an outcome gap and makes the claim (implicitly or explicitly) that the gap itself is evidence of systemic injustice, social scientists often just nod along with everyone else in the room.


pages: 428 words: 103,544

The Data Detective: Ten Easy Rules to Make Sense of Statistics by Tim Harford

access to a mobile phone, Ada Lovelace, affirmative action, algorithmic bias, Automated Insights, banking crisis, basic income, Black Swan, Bretton Woods, British Empire, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, Charles Babbage, clean water, collapse of Lehman Brothers, coronavirus, correlation does not imply causation, COVID-19, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Attenborough, Diane Coyle, disinformation, Donald Trump, Estimating the Reproducibility of Psychological Science, experimental subject, financial innovation, Florence Nightingale: pie chart, Gini coefficient, Hans Rosling, high-speed rail, income inequality, Isaac Newton, job automation, Kickstarter, life extension, meta-analysis, microcredit, Milgram experiment, moral panic, Netflix Prize, opioid epidemic / opioid crisis, Paul Samuelson, Phillips curve, publication bias, publish or perish, random walk, randomized controlled trial, recommendation engine, replication crisis, Richard Feynman, Richard Thaler, rolodex, Ronald Reagan, selection bias, sentiment analysis, Silicon Valley, sorting algorithm, statistical model, stem cell, Stephen Hawking, Steve Bannon, Steven Pinker, survivorship bias, universal basic income, W. E. B. Du Bois, When a measure becomes a target

An expert witness wasn’t so sure about the scientific evidence, and so he turned to the topic of storks and babies. There was a positive correlation between the number of babies born and the number of storks in the vicinity, he explained.17 That old story about babies being delivered by storks wasn’t true, the expert went on; of course it wasn’t. Correlation is not causation. Storks do not deliver children. But larger places have more room both for children and for storks. Similarly, just because smoking was correlated with lung cancer did not mean—not for a moment—that smoking caused cancer. “Do you honestly think there is as casual a relationship between statistics linking smoking with disease as there is about storks?”

Reading a newspaper or listening to talk radio also helped, but the effect of The Colbert Report was much bigger. One day a week of watching Colbert taught people as much about campaign finance as did four days a week reading a newspaper, for example—or five extra years of schooling. Of course, this is a measure of correlation, not causation. It’s possible that the people who were already interested in Super PACs tuned in to Colbert to hear him wisecrack about them. Or perhaps politics junkies know about Super PACs and also love watching Colbert. But I suspect the show did contribute to the growing understanding, because Colbert really did go deep into the details.

See also perspective on statistical data convergence of estimates, 246 conversion of units, 165n Corbett-Davies, Sam, 177 Corbyn, Jeremy, 14 coronavirus pandemic (COVID-19) and biases in journalism, 99, 102 and evaluation of statistical claims, 26, 29, 68, 68n and exponential growth, 41n and gender disparities, 140 and Nightingale Hospital, 213 and sanitary practices, 226n and selection bias in data, 109–10 statistical challenges of, 6–11, 120n and vaccination trends, 99 correlation vs. causation, 15, 64, 156–57, 275 corruption, 59, 209–10 cost-benefit analyses, 198, 199 Cotgreave, Andy, 232 COVID-19. See coronavirus pandemic (COVID-19) Cowperthwaite, John, 200–201, 203, 204 Crawford, Kate, 150 credibility of data, 192, 195 Credit Suisse, 80, 80n credulity in data, 164–67 Criado Perez, Caroline, 139 crime and criminal justice and alternative sanctions, 176–79 and bail recommendations, 158, 169, 180 and capital punishment, 35–36 and COMPAS system, 176–79 and human judgment vs. algorithms, 168–71 and missing data issues, 143 and murder rate data, 55, 87–89, 88n Crime Survey of England and Wales, 143 Crimean War, 213–14, 220, 226, 233–37 Cuddy, Amy, 121, 122 Cukier, Kenneth, 157 cultural influences, 37, 136–37, 201 curiosity, 265–79, 276n currency speculation, 255, 257–58 current offense bias, 169 curse of knowledge, 71–72 cyberspace, 149 cynicism and skepticism about statistics, 8–10, 13, 14, 266 Daily Mail, 133 dark data, 146 data recording practices, 66–67 dazzle camouflage, 218–19 De Meyer, Kris, 38–39 De Waarheid, 44 death penalty, 35–36 death rates, 214–15, 236.


pages: 523 words: 112,185

Doing Data Science: Straight Talk From the Frontline by Cathy O'Neil, Rachel Schutt

Amazon Mechanical Turk, augmented reality, Augustin-Louis Cauchy, barriers to entry, Bayesian statistics, bioinformatics, computer vision, correlation does not imply causation, crowdsourcing, data science, distributed generation, Edward Snowden, Emanuel Derman, fault tolerance, Filter Bubble, finite state, Firefox, game design, Google Glasses, index card, information retrieval, iterative process, John Harrison: Longitude, Khan Academy, Kickstarter, Mars Rover, Nate Silver, natural language processing, Netflix Prize, p-value, pattern recognition, performance metric, personalized medicine, pull request, recommendation engine, rent-seeking, selection bias, Silicon Valley, speech recognition, statistical model, stochastic process, tacit knowledge, text mining, the scientific method, The Wisdom of Crowds, Watson beat the top human players on Jeopardy!, X Prize

So, for example, your model for predicting or recommending a book on Amazon could include a feature “whether or not you’ve read Wes McKinney’s O’Reilly book Python for Data Analysis.” We wouldn’t say that reading his book caused you to read this book. It just might be a good predictor, which would have been discovered and come out as such in the process of optimizing for accuracy. We wish to emphasize here that it’s not simply the familiar correlation-causation trade-off you’ve perhaps had drilled into your head already, but rather that your intent when building such a model or system was not even to understand causality at all, but rather to predict. And that if your intent were to build a model that helps you get at causality, you would go about that in a different way.

He got his PhD in biostatistics from UC Berkeley after working at a litigation consulting firm. As part of his job, he needed to create stories from data for experts to testify at trial, and he thus developed what he calls “data intuition” from being exposed to tons of different datasets. Correlation Doesn’t Imply Causation One of the biggest statistical challenges, from both a theoretical and practical perspective, is establishing a causal relationship between two variables. When does one thing cause another? It’s even trickier than it sounds. Let’s say we discover a correlation between ice cream sales and bathing suit sales, which we display by plotting ice cream sales and bathing suit sales over time in Figure 11-1.

bipartite graphs, Kyle Teague and GetGlue, Terminology from Social Networks black box algorithms, Machine Learning Algorithms bootstrap aggregating, Random Forests bootstrap samples, Random Forests Bruner, Jon, Data Journalism–Writing Technical Journalism: Advice from an Expert C caret packages, Code readability and reusability case attribute vs. social network data, Case-Attribute Data versus Social Network Data causal effect, Definition: The Causal Effect causal graphs, Visualizing Causality causal inference, The Rubin Causal Model causal models, In-Sample, Out-of-Sample, and Causality–In-Sample, Out-of-Sample, and Causality, In-Sample, Out-of-Sample, and Causality causal questions, Asking Causal Questions causality, Causality–Three Pieces of Advice A/B testing for evaluation, A/B Tests–A/B Tests causal questions, Asking Causal Questions clinical trials to determine, The Gold Standard: Randomized Clinical Trials–The Gold Standard: Randomized Clinical Trials correlation vs., Correlation Doesn’t Imply Causation–Confounders: A Dating Example observational studies and, Second Best: Observational Studies–Definition: The Causal Effect OK Cupid example, OK Cupid’s Attempt–OK Cupid’s Attempt unit level, The Rubin Causal Model visualizing, Visualizing Causality–Visualizing Causality centrality measures, Centrality Measures–The Industry of Centrality Measures eigenvalue centrality, Representations of Networks and Eigenvalue Centrality usefulness of, The Industry of Centrality Measures channels, Word Frequency Problem problems with, Word Frequency Problem chaos, Thought Experiment: How Would You Simulate Chaos?


The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences by Rob Kitchin

Bayesian statistics, business intelligence, business process, cellular automata, Celtic Tiger, cloud computing, collateralized debt obligation, conceptual framework, congestion charging, corporate governance, correlation does not imply causation, crowdsourcing, data science, discrete time, disruptive innovation, George Gilder, Google Earth, Infrastructure as a Service, Internet Archive, Internet of things, invisible hand, knowledge economy, late capitalism, lifelogging, linked data, longitudinal study, Masdar, means of production, Nate Silver, natural language processing, openstreetmap, pattern recognition, platform as a service, recommendation engine, RFID, semantic web, sentiment analysis, slashdot, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart grid, smart meter, software as a service, statistical model, supply-chain management, the scientific method, The Signal and the Noise by Nate Silver, transaction costs

However, while pattern recognition might identify potentially interesting relationships, the veracity of these needs to be further tested on other datasets to ensure their reliability and validity. In other words, the relationships should form the basis for hypotheses that are more widely tested, which in turn are used to build and refine a theory that explains them. Thus correlations do not supersede causation, but rather should form the basis for additional research to establish if such correlations are indicative of causation. Only then can we get a sense as to how meaningful are the causes of the correlation. While the idea that data can speak for themselves free of bias or framing may seem like an attractive one, the reality is somewhat different.

Petabytes allow us to say: ‘Correlation is enough.’ We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot... Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all. There’s no reason to cling to our old ways. (my emphasis) Similarly, Prensky (2009) argues: ‘scientists no longer have to make educated guesses, construct hypotheses and models, and test them with data-based experiments and examples.

Data do not pre-exist their generation and arise from nowhere. Rather data are created within a complex data assemblage that actively shapes its constitution. Data then can never just speak for themselves, but are always, inherently, speaking from a particular position (Crawford 2013). Further, Anderson’s (2008) claim that ‘[c]orrelation supersedes causation’, suggests that patterns found within a dataset are inherently meaningful. This is an assumption that all trained statisticians know is dangerous and false. Correlations between variables within a dataset can be random in nature and have no or little causal association (see Chapter 9).


pages: 543 words: 153,550

Model Thinker: What You Need to Know to Make Data Work for You by Scott E. Page

"Robert Solow", Airbnb, Albert Einstein, Alfred Russel Wallace, algorithmic trading, Alvin Roth, assortative mating, Bernie Madoff, bitcoin, Black Swan, blockchain, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Checklist Manifesto, computer age, corporate governance, correlation does not imply causation, cuban missile crisis, data science, deliberate practice, discrete time, distributed ledger, en.wikipedia.org, Estimating the Reproducibility of Psychological Science, Everything should be made as simple as possible, experimental economics, first-price auction, Flash crash, Geoffrey West, Santa Fe Institute, germ theory of disease, Gini coefficient, High speed trading, impulse control, income inequality, Isaac Newton, John von Neumann, Kenneth Rogoff, knowledge economy, knowledge worker, Long Term Capital Management, loss aversion, low skilled workers, Mark Zuckerberg, market design, meta-analysis, money market fund, multi-armed bandit, Nash equilibrium, natural language processing, Network effects, opioid epidemic / opioid crisis, p-value, Pareto efficiency, pattern recognition, Paul Erdős, Paul Samuelson, phenotype, Phillips curve, pre–internet, prisoner's dilemma, race to the bottom, random walk, randomized controlled trial, Richard Feynman, Richard Thaler, school choice, scientific management, sealed-bid auction, second-price auction, selection bias, six sigma, social graph, spectrum auction, statistical model, Stephen Hawking, Supply of New York City Cabdrivers, tacit knowledge, The Bell Curve by Richard Herrnstein and Charles Murray, The Great Moderation, The Rise and Fall of American Growth, the rule of 72, the scientific method, The Spirit Level, the strength of weak ties, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, Tragedy of the Commons, urban sprawl, value at risk, web application, winner-take-all economy, zero-sum game

If the relationship is causal (see below), the model can be used to predict the number of orders that each employee can fill per shift as a function of years of work and we can use the model to project how many orders the current employees will fill next year. Here we have an instance of a model both making a prediction and guiding an action. Correlation vs. Causation Regression only reveals correlation among variables, not causality.3 If we first construct a model and then use regression to test if the model’s results are supported by data, we do not prove causality either. However, writing models first is far better than running regressions in search of a significant correlate, a technique known as data mining.

Income and wealth correlate with human flourishing. Higher-income individuals enjoy better health, longer life expectancy, and higher life satisfaction and happiness. Those at the bottom of the income distribution experience higher rates of homicide, divorce, mental illness, and anxiety.4 We must be careful not to confuse correlation with causation: a substantial part of this correlation can be explained by the fact that healthier, happier people earn more money. Nevertheless, almost all studies show a connection between income and flourishing. No one prefers to be poorer. Second, we have a plethora of models of inequality written by a diversely tooled collection of economists, sociologists, political scientists, and even physicists and biologists.

.), 233 conservatism, 89–90 consistency rational choice and, 50 in rational-actor model, 51 conspicuous consumption, 297 consumption, rational-actor model of, 48 consumption-investment equation, 101 content, 3 continuity, in rational-actor model, 49 continuous action games, 247–248 continuous function, entropy and, 146 continuous signals, 300 separation with, 301 convexity, 95–98 risk-loving and, 99 cooperation, 255 clustering bootstraps, 265 group selection and, 266 repetition and, 256–259 reputation and, 256–259 cooperative action model, 262–267 defining, 263 cooperative advantage, ratio of, 263 cooperative games, 108–110 defining, 108 coordination model, 241 paradoxes of, 174, 175 correlation, causation and, 86–87 costless signals, 298 craft traditions, 303 Craigslist, 105 Crime and Punishment (Dostoyevsky), 8 critical states, 74 Croson, Rachel, 243 crowded markets, 239 price competition in, 239 (fig.) crowds, wisdom of, 30 Cuban missile crisis, 9, 10 culture/strategy game, 318–319 cyclic, 147 data, 12 binary classifications of, 92–93 broadcast model and, 134 categories and, 34 dimensionality of, 31 in identification problem, 250 interpretation of, 2 many-model thinking and, 3–4 mining, 86 organization of, 2 overfitting, 41 on piece-rate work, 3–4 in wisdom hierarchy, 7 death rule, 176 decision problems, public project, 292 decisions, 47 Markov models, 199 trees, 93 decomposability, of entropy, 146 defection, 255 degeneration, 120 degree average, 118 of network formation, 123 in network structure, 118 of node, 118 degree squaring, 139–140 Deloitte, 4 Dennett, Daniel, 177–178 dependent variables, 84 depreciation rate, 101 design, 15 in REDCAPE, 20 Dewey, John, 305 Diamond, Jared, 269, 278 diffusion model, 134–137 diffusion probability, 135 dimensionality, of data, 31 diminishing returns, 98 discounting, hyperbolic, 52 discrete dynamical systems, 182 discrete signals, 298–300 Disney World, 185 distribution defining, 59 exponential, 149, 150 functions and, 63–66 lognormal, 60, 66–67 long-tailed, 59, 75–79 normal, 59–61, 61 (fig.), 65–66, 150 power-law, 69–73, 71 (fig.)


pages: 1,034 words: 241,773

Enlightenment Now: The Case for Reason, Science, Humanism, and Progress by Steven Pinker

3D printing, access to a mobile phone, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Albert Einstein, Alfred Russel Wallace, An Inconvenient Truth, anti-communist, Anton Chekhov, Arthur Eddington, artificial general intelligence, availability heuristic, Ayatollah Khomeini, basic income, Berlin Wall, Bernie Sanders, Black Swan, Bonfire of the Vanities, Brexit referendum, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, clockwork universe, cognitive bias, cognitive dissonance, Columbine, conceptual framework, correlation does not imply causation, creative destruction, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, data science, decarbonisation, deindustrialization, dematerialisation, demographic transition, Deng Xiaoping, distributed generation, diversified portfolio, Donald Trump, Doomsday Clock, double helix, Edward Jenner, effective altruism, Elon Musk, en.wikipedia.org, end world poverty, endogenous growth, energy transition, European colonialism, experimental subject, Exxon Valdez, facts on the ground, Fall of the Berlin Wall, first-past-the-post, Flynn Effect, food miles, Francis Fukuyama: the end of history, frictionless, frictionless market, Garrett Hardin, germ theory of disease, Gini coefficient, Hacker Conference 1984, Hans Rosling, hedonic treadmill, helicopter parent, Herbert Marcuse, Herman Kahn, Hobbesian trap, humanitarian revolution, Ignaz Semmelweis: hand washing, income inequality, income per capita, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), invention of writing, Jaron Lanier, Joan Didion, job automation, Johannes Kepler, John Snow's cholera map, Kevin Kelly, Khan Academy, knowledge economy, l'esprit de l'escalier, Laplace demon, life extension, long peace, longitudinal study, Louis Pasteur, Mahbub ul Haq, Martin Wolf, mass incarceration, meta-analysis, microaggression, Mikhail Gorbachev, minimum wage unemployment, moral hazard, mutually assured destruction, Naomi Klein, Nate Silver, Nathan Meyer Rothschild: antibiotics, negative emissions, Nelson Mandela, New Journalism, Norman Mailer, nuclear winter, obamacare, open economy, opioid epidemic / opioid crisis, Paris climate accords, Paul Graham, peak oil, Peter Singer: altruism, Peter Thiel, precautionary principle, precision agriculture, prediction markets, purchasing power parity, radical life extension, Ralph Nader, randomized controlled trial, Ray Kurzweil, rent control, Republic of Letters, Richard Feynman, road to serfdom, Robert Gordon, Rodney Brooks, rolodex, Ronald Reagan, Rory Sutherland, Saturday Night Live, science of happiness, Scientific racism, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Simon Kuznets, Skype, smart grid, sovereign wealth fund, stem cell, Stephen Hawking, Steve Bannon, Steven Pinker, Stewart Brand, Stuxnet, supervolcano, tech billionaire, technological singularity, Ted Kaczynski, The Rise and Fall of American Growth, the scientific method, The Signal and the Noise by Nate Silver, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, total factor productivity, Tragedy of the Commons, union organizing, universal basic income, University of East Anglia, Unsafe at Any Speed, Upton Sinclair, uranium enrichment, urban renewal, W. E. B. Du Bois, War on Poverty, We wanted flying cars, instead we got 140 characters, women in the workforce, working poor, World Values Survey, Y2K

Not surprisingly, as countries get richer they get happier (chapter 18); more surprisingly, as countries get richer they get smarter (chapter 16).59 In explaining this Somalia-to-Sweden continuum, with poor violent repressive unhappy countries at one end and rich peaceful liberal happy ones at the other, correlation is not causation, and other factors like education, geography, history, and culture may play roles.60 But when the quants try to tease them apart, they find that economic development does seem to be a major mover of human welfare.61 In an old academic joke, a dean is presiding over a faculty meeting when a genie appears and offers him one of three wishes—money, fame, or wisdom.

That creates an opening for politicians to rouse the rabble by singling out cheaters who take more than their fair share: welfare queens, immigrants, foreign countries, bankers, or the rich, sometimes identified with ethnic minorities.18 In addition to effects on individual psychology, inequality has been linked to several kinds of society-wide dysfunction, including economic stagnation, financial instability, intergenerational immobility, and political influence-peddling. These harms must be taken seriously, but here too the leap from correlation to causation has been contested.19 Either way, I suspect that it’s less effective to aim at the Gini index as a deeply buried root cause of many social ills than to zero in on solutions to each problem: investment in research and infrastructure to escape economic stagnation, regulation of the finance sector to reduce instability, broader access to education and job training to facilitate economic mobility, electoral transparency and finance reform to eliminate illicit influence, and so on.

In the developing world a young woman can’t even work as a household servant if she is unable to read a note or count out supplies, and higher rungs of the occupational ladder require ever-increasing abilities to understand technical material. The first countries that made the Great Escape from universal poverty in the 19th century, and the countries that have grown the fastest ever since, are the countries that educated their children most intensely.5 As with every question in social science, correlation is not causation. Do better-educated countries get richer, or can richer countries afford more education? One way to cut the knot is to take advantage of the fact that a cause must precede its effect. Studies that assess education at Time 1 and wealth at Time 2, holding all else constant, suggest that investing in education really does make countries richer.


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10% Less Democracy: Why You Should Trust Elites a Little More and the Masses a Little Less by Garett Jones

"Robert Solow", Andrei Shleifer, Asian financial crisis, Brexit referendum, business cycle, central bank independence, clean water, corporate governance, correlation does not imply causation, creative destruction, Edward Glaeser, financial independence, game design, German hyperinflation, hive mind, invisible hand, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", Jean Tirole, Kenneth Rogoff, Mark Zuckerberg, mass incarceration, military-industrial complex, minimum wage unemployment, Mohammed Bouazizi, open economy, Pareto efficiency, Paul Samuelson, price stability, rent control, The Wealth of Nations by Adam Smith, trade liberalization, Tyler Cowen

If, as neoclassical Nobel-winning economists like Lucas, Sargent, and Friedman said, the main job of a central bank is to maintain a low average rate of inflation, then it’s clear that the politically disconnected central banks are the ones that are getting the job done. FIGURE 3.1. Central Bank Independence and Inflation: A Negative Relationship. Source: Alesina and Summers (1993). Of course, any time you see data plotted out like this, with a strong correlation like this one, you should remind yourself that correlation isn’t causation—that having a chandelier in your house doesn’t make you rich (even though it’s a sign you’re rich), that buying a baby stroller won’t make you a parent (though it’s a sign a baby is on the way). But then what is causation? How can we know whether it’s the legal independence of the central banks of the United States, Switzerland, and Germany that is getting the job done?

I’m taking that approach here, though in a nontechnical way: noting that multiple kinds of evidence, multiple measures of central bank independence, point toward the same prediction. The less political, the less democratic, the more insider driven the nation’s central bank is, the better the outcomes. A bundle of correlations tied together with some suggestions of causation. Oh, and here’s a small bonus in this tire-kicking line of research. Cukierman, like others, doesn’t find any noticeable evidence from the rich countries that central bank independence makes your country richer. You get lower, more stable inflation (which voters love), plus a more stable unemployment rate and a more stable economy overall, and those are great benefits.

However at least two qualifications need to be entered. First, the notably negative correlation between central bank independence and inflation . . . is not very robust. For example, it does not hold up . . . when other variables are considered. . . . Second, some recent studies have questioned whether correlation implies causation.¹⁵ Despite the muddled empirical evidence, Blinder, who has been a central banker himself and has met many more central bankers the world over, still takes it as pretty obvious that central banks should be insulated, at least to some degree, from the political process. That said, he is no full-throated critic of democracy.


pages: 360 words: 85,321

The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling by Adam Kucharski

Ada Lovelace, Albert Einstein, Antoine Gombaud: Chevalier de Méré, beat the dealer, Benoit Mandelbrot, butterfly effect, call centre, Chance favours the prepared mind, Claude Shannon: information theory, collateralized debt obligation, Computing Machinery and Intelligence, correlation does not imply causation, diversification, Edward Lorenz: Chaos theory, Edward Thorp, Everything should be made as simple as possible, Flash crash, Gerolamo Cardano, Henri Poincaré, Hibernia Atlantic: Project Express, if you build it, they will come, invention of the telegraph, Isaac Newton, Johannes Kepler, John Nash: game theory, John von Neumann, locking in a profit, Louis Pasteur, Nash equilibrium, Norbert Wiener, p-value, performance metric, Pierre-Simon Laplace, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, Richard Feynman, Ronald Reagan, Rubik’s Cube, statistical model, The Design of Experiments, Watson beat the top human players on Jeopardy!, zero-sum game

Horses in Hong Kong win because they are familiar with the terrain, and they are familiar with it because they have run lots of races. But just because two things are apparently related—like probability of winning and number of races run—it doesn’t mean that one directly causes the other. An oft-quoted mantra in the world of statistics is that “correlation does not imply causation.” Take the wine budget of Cambridge colleges. It turns out that the amount of money each Cambridge college spent on wine in the 2012–2013 academic year was positively correlated with students’ exam results during the same period. The more the colleges spent on wine, the better the results generally were.

“Were England the Uunluckiest Team in the World Cup Group Stages?” FT Data Blog. 29 June 2014. http://blogs.ft.com/ftdata/2014/06/29/were-england-the-unluckiest-team-in-the-world-cup-group-stages/. 206Cambridge college spent on wine: “In Vino Veritas, Redux.” The Economist, February 5, 2014. http://www.economist.com/blogs/freeexchange/2014/02/correlation-and-causation-0. 207topped the wine list with a spend of £338,559: Simons, John. “Wages Not Wine: Booze Hound Colleges Spend £3 million on Wine.” Tab (Cambridge, England), January 22, 2014. http://thetab.com/uk/cambridge/2014/01/22/booze-hound-colleges-spend-3-million-on-wine-32441. 207Countries that consume lots of chocolate: Messerli, F.

See robots (bots) computerized prediction in blackjack, 42 in checkers, 156, 157 in horse racing, 46, 51, 57, 68 and the Monte Carlo method, 61 in roulette, 2, 13, 14, 15–20, 22 in sports, 80–82, 87, 88, 89–90, 97, 105, 217 “Computing Machinery and Intelligence” (Turing), 175 Connect Four, 158–159 control over events, 199 controlled randomness, 25–26, 28 cooperative relationships, 129, 136 copycats, 132 Coram, Marc, 63, 64 correlation and causation, issue of, 206–207 Corsi rating, 85 Cosmopolitan, Las Vegas 87 countermeasures, 21, 86, 195, 214 counting cards. See card counting Crick, Francis, 23 cricket, 90 curiosity, following, 218 Dahl, Fredrik, 172–173, 175, 176, 177, 182–183, 184, 185 Darwin, Charles, 46 data access to, 142 availability of, 54, 55, 68, 73, 86, 102, 174, 209 better, sports analysis methods and access to, 207, 217 binary, 116 collecting as much as possible, 4–5, 103 enough, to test strategies, 131 faster transatlantic travel of, 113 juggling, 166 limited, 84 new, testing strategies against, 53, 54 statistics and, importance of, in sports, 79, 80 storage and communication of, 11 data chunks, memory capacity and size of, 179–180 Deceptive Interaction Task, 190–191 decision making, chaotic, 162 decision-making layers, 173–174 Deep Blue chess computer, 166, 167, 171, 176 Deep Thought chess computer, 167 DeepFace Facebook algorithm, 174–175 DeRosa, David, 198–199, 200 Design of Experiments, The (Fisher), 24 deterministic game, 156 Diaconis, Persi, 62–63 DiCristina, Lawrence, 198, 199, 200, 201 Dixon, Mark, 74, 75, 76–78, 82, 97–98, 107, 218 Djokovic, Novak, 110 Dobson, Andrew, 129 Dodds, Peter Sheridan, 203 dogma, avoiding, 218 dovetail shuffle, 41–42, 62 Dow Jones Industrial Average, 96, 121, 122 Drug Enforcement Administration, 214 eBay, 94 Econometrica (journal), 148 economic theory, 153 ecosystems, 125–129, 130–131, 133 Einstein, Albert, 210 endgame database, 159–160 English Draughts Association, 156 English Premier League, 209 Enigma machines, 169–170 Eslami, Ali, 185–186, 187 Ethier, Stewart, 7–8 Eudaemonic Pie, The (Bass), 14, 15 Eudaemonic prediction method, 14, 15–20, 22, 124, 208 Euro 2008 soccer tournament, 76 European Championship (soccer tournament), 111 European currency union, 129 every-day gamblers, 102, 107 exchange rate, 110 exchanges.


The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do by Erik J. Larson

AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Amazon Mechanical Turk, artificial general intelligence, autonomous vehicles, Big Tech, Black Swan, Boeing 737 MAX, business intelligence, Charles Babbage, Claude Shannon: information theory, Computing Machinery and Intelligence, conceptual framework, correlation does not imply causation, data science, Elon Musk, Ernest Rutherford, Filter Bubble, Georg Cantor, hive mind, ImageNet competition, information retrieval, invention of the printing press, invention of the wheel, Isaac Newton, Jaron Lanier, Jeff Hawkins, John von Neumann, Kevin Kelly, Law of Accelerating Returns, Loebner Prize, Nate Silver, natural language processing, Norbert Wiener, PageRank, PalmPilot, pattern recognition, Peter Thiel, Ray Kurzweil, retrograde motion, self-driving car, semantic web, Silicon Valley, social intelligence, speech recognition, statistical model, Stephen Hawking, superintelligent machines, tacit knowledge, technological singularity, The Coming Technological Singularity, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, theory of mind, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, Yochai Benkler

Depressingly for Markram and other advocates of Data Brain proj­ects like the H ­ uman Brain Proj­ect, overfitting is particularly problematic in the absence of causal or theoretical information about a domain—in the absence of general A I M y­thol­ogy I nvades N euroscience 259 intelligence, that is. “Correlation is not causation” is a familiar caution, but bold claims made on behalf of Big Data AI proj­ects in recent years have made it particularly relevant again. Apparently obvious truths about knowledge are seemingly now in need of restatement in the wake of major claims made about data and machine learning.

., 248 Byron, Lord, 238 Capek, Karel, 82–83 causation: correlation and, 259; Hume on, 120; ladder of, 130–131, 174; relevance prob­lems in, 112 chess: Deep Blue for, 219; played by computers, 284n1; Turing’s interest in, 19–20 Chollet, François, 27 Chomsky, Noam, 52, 95 classification, in supervised learning, 134 cognition, Legos theory of, 266 color, 79, 289n16 common sense, 2, 131–132, 177; scripts approach to, 181–182; Winograd schemas test of, 196–203 computational knowledge, 178–182 computers: chess played by, 19–20, 284n1; earliest, 232–233; in history of technology, 44; machine learning by, 133; translation by, 52–55; as Turing machines, 16, 17; Turing’s paper on, 10–11 Comte, August, 63–66 Condorcet (Marie Jean Antoine Nicolas Caritat, the Marquis de Condorcet), 288n4 conjectural inference, 163 consciousness, 77–80, 277 conversations, Grice’s Maxims for, 215–216 Copernicus, Nicolaus, 104 counterfactuals, 174 creative abduction, 187–189 Cukier, Kenneth, 143, 144, 257 Czecho­slo­va­k ia, 60–61 Dartmouth Conference (Dartmouth Summer Research Proj­ect on Artificial Intelligence; 1956), 50–51 data: big data, 142–146; observations turned into, 291n12 I ndex Data Brain proj­ects, 251–254, 261, 266, 268, 269 data science, 144 Davis, Ernest, 131, 183; on brittleness prob­lem, 126; on correlation and causation, 259; on DeepMind, 127, 161–162; on Google Duplex, 227; on limitations of AI, 75–76; on machine reading comprehension, 195; on Talk to Books, 228 deduction, 106–110, 171–172; extensions to, 167, 175; knowledge missing from, 110–112; relevance in, 112–115 deductive inference, 189 Deep Blue (chess computer), 219 deep learning, 125, 127, 134, 135; as dead end, 275; fooling systems for, 165–166; not used by Watson, 231 DeepMind (computer program), 127, 141, 161–162 DeepQA (Jeopardy!

See natu­ral language Lanier, Jaron, 84, 244, 277; on encouraging ­human intelligence, 239; on erosion of personhood, 270, 272–273 Large Hadron Collider (LHC), 254–255 Law of Accelerating Returns (LOAR), 42, 47–48 learning: definition of, 133; by ­humans, 141 LeCun, Yann, 75 Legos theory of cognition, 266 Lenat, Doug, 74 Levesque, Hector, 76, 216; on attempts at artificial general intelligence, 175, 186; on Goostman, 192; pronoun disambiguation prob­lem of, 203; on Winograd schemas, 195–196, 198–201 Loebner Prize, 59 Logic Theorist (AI program), 51, 110 Lord of the Rings (novels; Tolkien), 229–230 Lovelace, Ada, 233 Marcus, Gary, 131, 183; on brittleness prob­lem, 126; on correlation and causation, 259; on DeepMind, 127, 161–162; on Google Duplex, 227; on Goostman, 192; on Kurzweil’s pattern recognition theory, 265; on limitations of AI, 75–76; on machine reading comprehension, 195; on superintelligent computers, 81; on Talk to Books, 228 Markram, Henry, 252–254, 273; on AI, 251; on big data versus theory, 256–258, 267, 268; on hive mind, 245–246, 276; H ­ uman Brain Proj­ect ­u nder, 247–250; Legos theory of cognition by, 266; on theory in neuroscience, 261, 262 Marquand, Alan, 232 mathe­matics: functions in, 139; Gödel’s incompleteness theorems in, 12–14; Hilbert’s challenge in, 14–16; Turing on intuition and ingenuity in, 11 Mathews, Paul M., 256, 267 Mayer-­Schönberger, Viktor, 143, 144, 257 McCarthy, John, 50, 107, 285n11 Microsoft Tay (chatbot), 229 Mill, John Stuart, 242, 243 machine learning: definition of, 133; Miller, George, 50 empirical constraint in, 146–149; minimax technique, 284n1 frequency assumption in, 150–154; Minsky, Marvin, 50, 52, 222 Mitchell, Melanie, 165 model saturation in, 155–156; as narrow AI, 141–142; as simulation, Mitchell, Tom, 133 138–140; supervised learning in, model saturation, 155–156 modus ponens, 108–109, 168–169 137 machine learning systems, 28–30 monologues, Turing test variation using, 194–195, 212–214 MacIntyre, Alasdair, 70–71 I ndex monotonic inference, 167 Mountcastle, Vernon, 264 Mumford, Lewis, 95, 98 “The Murders in the Rue Morgue” (short story, Poe), 89–94 Musk, Elon, 1, 75, 97 narrowness, 226–231 Nash, John, 50 National Resource Council (NRC), 53, 54 natu­ral language: AI understanding to, 228–229; computers’ understanding of, 48, 51–55; context of, 204; continued prob­lems with translation of, 56–57; in speech-­ driven virtual assistance applications, 227; Turing test of, 50, 194; understanding and meaning of, 205–214; Winograd schemas test of, 195–203 neocortical theories: Hawkins’s, 263; Kurzweil’s, 264–266 neural networks, 75 neuroscience, 246; collaboration in, 245–247; Data Brain proj­ects in, 251–254; ­Human Brain Proj­ect in, 247–251; neocortical theories in, 263–268; theory versus big data in, 255–256, 261–262 Newell, Allan, 51, 110 news stories, 152–154 Newton, Isaac, 187, 276 Nietz­sche, Friedrich Wilhelm, 63 no f­ ree lunch theorem, 29 noisy channel approach, 56 non-­monotonic inference, 167–168 normality assumption, 150–151 309 Norvig, Peter, 77, 155, 156 nuclear weapons, 45 Numenta (firm), 263 observation: generalizing from, 117–118; in induction, 115; limitations of, 121; turning into data, 291n12 operant conditioning (behaviorism), 69 orthography, 205 overfitting (statistical), 258–261 Page, Larry, 56 Pearl, Judea, 130–131, 174, 291n13 Peirce, Charles Sanders, 95–99; on abduction, 25–26, 160–168; on abductive inference, 99–102, 190; on guessing, 94, 183–184; on “Logical Machines,” 232–233, 273; theft of watch from, 157–160, 289–290n5; on types of inference, 171–172, 181; on weight of evidence, 24 Peirce, Juliette, 98 Perin, Rodrigo, 266 PIQUANT (AI system), 221–224 Poe, Edgar Allan, 89–94, 99, 102 Polanyi, Michael, 73–74 Popper, Karl, 70–71, 122 positivism, 63 pragmatics (context for natu­ral language), 204, 206, 214–215, 296n1 predictions, 69–73; big data used for, 143–144; induction in, 116, 124; limits to, 130 predictive neuroscience, 254 probabilistic inference, 102 programming languages for early computers, 284n2 310 I ndex scripts, 181–182 se­lection prob­lem, 182–184, 186–190 self-­d riving cars, 127, 278; saturation prob­lem in, 155–156 random sampling, 118 self-­reference, in mathe­matics, 13 reading comprehension, 195 semantic role labeling, 138–139 real-­t ime inference, 101 semantics, 206 reasoning, 176 Semantic Web, 179 religion, 63 semi-­supervised learning, 133–134 sequential classification, 136–137 resource description framework sequential learning, 136–137 (RDF), 179 R.U.R.


pages: 1,380 words: 190,710

Building Secure and Reliable Systems: Best Practices for Designing, Implementing, and Maintaining Systems by Heather Adkins, Betsy Beyer, Paul Blankinship, Ana Oprea, Piotr Lewandowski, Adam Stubblefield

anti-pattern, barriers to entry, bash_history, business continuity plan, business process, Cass Sunstein, cloud computing, continuous integration, correlation does not imply causation, create, read, update, delete, cryptocurrency, cyber-physical system, database schema, Debian, defense in depth, DevOps, Edward Snowden, fault tolerance, fear of failure, general-purpose programming language, Google Chrome, information security, Internet of things, Kubernetes, load shedding, margin call, microservices, MITM: man-in-the-middle, operational security, performance metric, pull request, ransomware, revision control, Richard Thaler, risk tolerance, self-driving car, Skype, slashdot, software as a service, source of truth, Stuxnet, Turing test, undersea cable, uranium enrichment, Valgrind, web application, Y2K, zero day

We then examined the code that started threads, and discovered that many libraries just picked a thread size at random, explaining the variation of sizes. This understanding enabled us to save memory by focusing on the few threads with large stacks. Be mindful of correlation versus causation Sometimes debuggers assume that two events that start at the same time, or that exhibit similar symptoms, have the same root cause. However, correlation does not always imply causation. Two mundane problems might occur at the same time but have different root causes. Some correlations are trivial. For example, an increase in latency might lead to a reduction in user requests, simply because users are waiting longer for the system to respond.

-Triaging the Incident when tools try to be helpful, Operational Security cross-site scripting (XSS), Preventing XSS: SafeHtml cryptographic code, Example: Secure cryptographic APIs and the Tink crypto framework-Example: Secure cryptographic APIs and the Tink crypto framework cryptographic keys, Credential and Secret Rotation cryptography, The Roles of Specialists CSRs (Certificate Signing Requests), Programming Language Choice culture, A Note About Culture, Building a Culture of Security and Reliability-Conclusionaligning goals and participant incentives, Align Project Goals and Participant Incentives balancing accountability and risk taking, Culture of Yes building a case for change, Build a Case for Change building a culture of security and reliability, Building a Culture of Security and Reliability-Conclusion building empathy, Build Empathy changing through good practice, Changing Culture Through Good Practice-Build Empathy culture of awareness, Culture of Awareness-Culture of Awareness culture of inevitability, Culture of Inevitably culture of review, Culture of Review-Culture of Review culture of sustainability, Culture of Sustainability-Culture of Sustainability culture of yes, Culture of Yes defining a healthy security/reliability culture, Defining a Healthy Security and Reliability Culture-Culture of Sustainability escalations and problem resolution, Escalations and Problem Resolution increasing productivity and usability, Increase Productivity and Usability-Increase Productivity and Usability leadership buy-in for security/reliability changes, Convincing Leadership-Escalations and Problem Resolution least privilege's impact on, Impact on Collaboration and Company Culture overcommunication and transparency, Overcommunicate and Be Transparent picking your battles, Pick Your Battles reducing fear with risk-reduction mechanisms, Reduce Fear with Risk-Reduction Mechanisms-Reduce Fear with Risk-Reduction Mechanisms safety nets as norm, Make Safety Nets the Norm security and reliability as default condition, Culture of Security and Reliability by Default culture of no, Culture of Yes culture of yes, Culture of Yes CVD (coordinated vulnerability disclosure), Compromises Versus Bugs Cyber Grand Challenge, Automation and Artificial Intelligence Cyber Kill Chain, Cyber Kill Chains™ cyber warfare, Military purposes D Dapper, Improve observability DARPA (Defense Advanced Research Projects Agency), Automation and Artificial Intelligence data corruption, Distinguish horses from zebras data integrity, Integrity data isolation, Data isolation data plane, Example: Google’s frontend design data sanitization, Data Sanitization data summarization, Budget for Logging DDoS attacks (see distributed denial-of-service attacks) debugging, Debugging Techniques-Practice!cleaning up code, Clean up code collaborative debugging, Collaborative Debugging: A Way to Teach correlation-versus-causation problem, Be mindful of correlation versus causation data corruption and checksums, Distinguish horses from zebras deleting legacy systems, Delete it! distinguishing common from uncommon bugs, Distinguish horses from zebras filtering out normal events from bugs, Know what’s normal for your system hypothesis testing with actual data, Test your hypotheses with actual data importance of regular practice, Practice!

In digital forensics, the relationships between events are as important as the events themselves. Much of the work a forensic analyst does to obtain artifacts contributes to the goal of building a forensic timeline.5 By collecting a chronologically ordered list of events, an analyst can determine correlation and causation of attacker activity, proving why these events happened. Example: An Email Attack Let’s consider a fictional scenario: an unknown attacker has successfully compromised an engineer’s workstation by sending a malicious attachment via email to a developer, who unwittingly opened it. This attachment installed a malicious browser extension onto the developer’s workstation.


pages: 348 words: 99,383

The Financial Crisis and the Free Market Cure: Why Pure Capitalism Is the World Economy's Only Hope by John A. Allison

Affordable Care Act / Obamacare, Alan Greenspan, American ideology, bank run, banking crisis, Bear Stearns, Bernie Madoff, business cycle, clean water, collateralized debt obligation, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, disintermediation, fiat currency, financial innovation, Fractional reserve banking, full employment, Greenspan put, high net worth, housing crisis, invisible hand, life extension, low skilled workers, market bubble, market clearing, minimum wage unemployment, money market fund, moral hazard, negative equity, obamacare, Paul Samuelson, price mechanism, price stability, profit maximization, quantitative easing, race to the bottom, reserve currency, risk/return, Robert Shiller, The Bell Curve by Richard Herrnstein and Charles Murray, too big to fail, transaction costs, Tyler Cowen, yield curve, zero-sum game

However, the models used in physics capture causal relationships and are properly evaluated based on the predictive power of these causal relationships. However, in economics, practically all mathematical models capture correlations, not causations. There is a difference in kind between correlation and causation. Also, the models are based on a multitude of assumptions. The danger lies in placing far too much confidence in models based on correlation rather than causation. Economists and government regulators often fall into the trap of believing that these models are objective. However, there are important economic factors, such as human behavior, that cannot be clearly mathematized.

In reality, the tails often turn out to be “fat,” that is, to have a greater chance of occurring than the model suggests. The tails typically represent very positive and very negative outcomes. In the case of the financial crisis, the negative fat tails (improbable events) became reality. These tails were magnified by the effect of panic on human behavior under stress. All the correlations (which were not based on causation) fell apart when human beings, who make decisions, started reacting to negative news. In addition, it is easy to underestimate the likelihood of unlikely events. For example, if you build a house in a 100-year flood plain, you will at some point experience a flood. It may be 90 years from now, or it may be next week.


pages: 578 words: 168,350

Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies by Geoffrey West

Alfred Russel Wallace, Anton Chekhov, Benoit Mandelbrot, Black Swan, British Empire, butterfly effect, caloric restriction, caloric restriction, carbon footprint, Cesare Marchetti: Marchetti’s constant, clean water, coastline paradox / Richardson effect, complexity theory, computer age, conceptual framework, continuous integration, corporate social responsibility, correlation does not imply causation, cotton gin, creative destruction, dark matter, Deng Xiaoping, double helix, Edward Glaeser, endogenous growth, Ernest Rutherford, first square of the chessboard, first square of the chessboard / second half of the chessboard, Frank Gehry, Geoffrey West, Santa Fe Institute, Guggenheim Bilbao, housing crisis, Index librorum prohibitorum, invention of agriculture, invention of the telephone, Isaac Newton, Jane Jacobs, Jeff Bezos, Johann Wolfgang von Goethe, John von Neumann, Kenneth Arrow, laissez-faire capitalism, life extension, Mahatma Gandhi, mandelbrot fractal, Marc Benioff, Marchetti’s constant, Masdar, megacity, Murano, Venice glass, Murray Gell-Mann, New Urbanism, Peter Thiel, profit motive, publish or perish, Ray Kurzweil, Richard Feynman, Richard Florida, Salesforce, Silicon Valley, smart cities, Stephen Hawking, Steve Jobs, Stewart Brand, technological singularity, The Coming Technological Singularity, The Death and Life of Great American Cities, the scientific method, the strength of weak ties, time dilation, too big to fail, transaction costs, urban planning, urban renewal, Vernor Vinge, Vilfredo Pareto, Von Neumann architecture, Whole Earth Catalog, Whole Earth Review, wikimedia commons, working poor

Just relying on data alone, or even mathematical fits to data, without having some deeper understanding of the underlying mechanism is potentially deceiving and may well lead to erroneous conclusions and unintended consequences. This admonition is closely related to the classic warning that “correlation does not imply causation.” Just because two sets of data are closely correlated does not imply that one is the cause of the other. There are many bizarre examples that illustrate this point.4 For instance, over the eleven-year period from 1999 to 2010 the variation in the total spending on science, space, and technology in the United States almost exactly followed the variation in the number of suicides by hanging, strangulation, and suffocation.

In a highly provocative article published in Wired magazine in 2008 titled “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete,” its then editor, Chris Anderson, wrote: The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all . . . faced with massive data, this approach to science—hypothesize, model, test—is becoming obsolete. . . . Out with every theory of human behavior, from linguistics to sociology.

There are many versions of these, but all of them are based on the idea that we can design and program computers and algorithms to evolve and adapt based on data input to solve problems, reveal insights, and make predictions. They all rely on iterative procedures for finding and building upon correlations in data without concern for why such relationships exist and implicitly presume that “correlation supersedes causation.” This approach has become a huge area of interest and has already had a big impact on our lives. For instance, it is central to how search engines like Google operate, how strategies for investment or operating an organization are devised, and it provides the foundational basis for driverless cars.


pages: 375 words: 102,166

The Genetic Lottery: Why DNA Matters for Social Equality by Kathryn Paige Harden

23andMe, Affordable Care Act / Obamacare, assortative mating, Bayesian statistics, Berlin Wall, clean water, combinatorial explosion, coronavirus, correlation coefficient, correlation does not imply causation, COVID-19, crowdsourcing, delayed gratification, deliberate practice, desegregation, double helix, epigenetics, game design, George Floyd, Gregor Mendel, impulse control, income inequality, Jeff Bezos, longitudinal study, low skilled workers, Mark Zuckerberg, meta-analysis, Monkeys Reject Unequal Pay, phenotype, randomized controlled trial, replication crisis, Scientific racism, stochastic process, surveillance capitalism, The Bell Curve by Richard Herrnstein and Charles Murray, twin studies, War on Poverty, zero-sum game

As I described in the previous chapter, a GWAS correlates small bits of DNA with an outcome, but, as is the common refrain—correlation does not equal causation. How do we get from the correlational results of GWAS to an understanding of how genes may be a cause of social inequalities in a particular historical and cultural context? Answering that question, in turn, requires us to be precise about our definition of the word “cause,” and it is to that topic that we turn our attention in the next chapter. 5 A Lottery of Life Chances Every Psychology 101 student knows that “correlation does not equal causation.” Restaurants that add more grated sea urchin to every dish might be rated higher on Yelp, but that correlation does not mean that adding sea urchin to every menu is going to cause people to enjoy restaurants more.

If researchers had selected, for example, all the boys to remain in the orphanage and all the girls to go to foster care, there would be no way of telling if the statistical signal that is being detected is driven by being in foster care or being female. Part of the reason why every first-year undergraduate is told, at some point, that “correlation does not equal causation” is a variation on that point. Yes, volume of ice cream sales in a county are positively correlated with murder rates, but eating lots of ice cream isn’t the only thing that those counties have in common—they also share being in warmer climates. Comparing groups of people in order to peer into a counterfactual world only works if you can isolate X from everything else that differs between people.

Because these different factors are braided together, parents talking more to their young children might be correlated with how well those children do in school, but that doesn’t mean that changing how much parents talk to their children will make a difference in how well those children do in school. We are back to the idea that correlation does not equal causation. The idea that genetic differences between people are braided together with the environmental differences that social scientists seek to understand and change can be met with hostility. When I wrote in the New York Times that genetic research related to education would be helpful for understanding environmental levers for change,12 the sociologist Ruha Benjamin accused me of engaging in “savvy slippage between genetic and environmental factors that would make the founders of eugenics proud.”13 But the “slippage” between genetic and environmental factors is not an invention of eugenic ideology.


pages: 414 words: 119,116

The Health Gap: The Challenge of an Unequal World by Michael Marmot

active measures, active transport: walking or cycling, Affordable Care Act / Obamacare, Atul Gawande, Bonfire of the Vanities, Broken windows theory, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, Celtic Tiger, centre right, clean water, congestion charging, correlation does not imply causation, Doha Development Round, epigenetics, financial independence, future of work, Gini coefficient, Growth in a Time of Debt, illegal immigration, income inequality, Indoor air pollution, Kenneth Rogoff, Kibera, labour market flexibility, longitudinal study, lump of labour, Mahatma Gandhi, Mahbub ul Haq, meta-analysis, microcredit, New Urbanism, obamacare, paradox of thrift, race to the bottom, Rana Plaza, RAND corporation, road to serfdom, Simon Kuznets, Socratic dialogue, structural adjustment programs, the built environment, The Spirit Level, trickle-down economics, twin studies, urban planning, Washington Consensus, Winter of Discontent, working poor

Should we really assume that these dark satanic mills and airless places, rather than causing terrible illness and shortened lives, selectively employed and attracted as residents sick people and those whose backgrounds accounted for all their subsequent illness? That subsequent improvement in living and working conditions, thus abating Victorian squalor, and associated improvements in health, were correlation, not causation? That while medical care improved health, housing also got better, and an intellectually slack public health profession mistook the improvement in housing and working conditions for causes of improved health? If proponents of this set of assumptions dropped their guard for a moment and accepted the evidence that air pollution, crowded living space, ghastly working conditions and poor nutrition were causes of ill-health in Victorian times why, a priori, do they start from the position that living and working conditions are not a cause of ill-health in the twenty-first century?

A review of 124 studies confirmed that child physical abuse, emotional abuse and neglect (they did not study sexual abuse) are linked to adult mental disorders, suicide attempts, drug use, sexually transmitted infections and risky sexual behaviour.9 The authors of the review concluded that this is more than simple correlation but represents causation. The graded nature of the relation between abuse and adult mental, and perhaps physical, ill-health – the more types of abuse the worse the adult health – suggests that we should not be looking only at exceptional episodes of abuse but, more generally, at quality of early child development.

FIGURE 6.3: GETTING INTO WORK IN SWANSEA AND WREXHAM By focusing on the problem in a strategic way, working with young people, giving them access to information, and perhaps above all, caring, authorities in these towns lowered the toll of young people not in employment, education or training. There was an unexpected benefit. Youth offending in Swansea fell from over 1,000 incidents a year to fewer than 400.33 Correlation is not causation. One cannot say that the reduction in NEETs was responsible for the reduction in youth offending, but it is certainly possible. Unemployment harms health and work is vital. When work is of ‘good’ quality it is empowering. It provides power, money and resources – all essential for a healthy life.


pages: 533

Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, algorithmic bias, Andrew Keen, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, bitcoin, blockchain, Boeing 747, brain emulation, Brexit referendum, British Empire, business process, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, cloud computing, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, crowdsourcing, cryptocurrency, data science, digital map, disinformation, distributed ledger, Donald Trump, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, Filter Bubble, future of work, Future Shock, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, lifelogging, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Bannon, Steve Jobs, Steve Wozniak, Steven Levy, tech bro, technological singularity, the built environment, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, Tragedy of the Commons, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, working-age population, Yochai Benkler

Or they fall foul of the group membership fallacy: the fact that I am a member of a group that tends to have a particular characteristic does not necessarily mean that I share that characteristic (a point sometimes lost on probabilistic machine learning approaches).There’s the entrenchment problem: it may well be true that students from higher-income families are more likely to get better grades at university, but using family income as an admission criterion would obviously entrench the educational inequality that already exists.8 There’s the correlation/ causation fallacy: the data may tell you that people who play golf tend to do better in business, but that does not mean that business success is caused by playing golf (and to hire on that basis might contradict a principle of justice which says that hiring should be done on merit).These are just a few examples—but given what we know of human ignorance and prejudice, we can be sure they aren’t the only ones.

Gabriella 180, 181, 404 collaborative democracy see Wiki Democracy Collini, Stefan 407 Collins,Victor 134 command economy 265, 329 commons 331–4, 335 commons-based peer production 244 communication liberty and private power 190–1 perception-control 148, 150–1, 229 communism 12 ‘fully automated luxury’ 328 community, freedom of see republican freedom companionship, as robot function 55 Compas 174 competition law 357 competitive elitism 217–19, 221, 240, 242, 253, 254 Computerscience.org 423 computing power, growth in 37–41 Comte, Auguste 170, 175, 177, 250, 403, 417 concentration camp inmates 131 495 concentration of wealth and power 318–22, 329–30 sharing economy 336 conceptual analysis 81–3, 84–5 Condliffe, Jamie 375 Condorcet, Marquis de 224 Conger, Krista 372 connectivity of technology 44–8 Connolly, William E. 390 consent principle 351–2, 353, 355, 357 Constant, Benjamin 128, 395 constitutive nature of technology 53–7 contextual analysis 84–5 contracts, smart see smart contracts Cooper, Daniel 402 Copernicus, Nicolaus 14 copyright 324, 332, 333 infringement 156 Cornell University 57 correlation/causation fallacy, rule-based injustice 284 corruption 82, 84, 225, 329, 361 Costeja González, Mario 138 Couldry, Nick 421 counters (democracy theorists) 224–5 Crawford, Kate 418 Creative Commons 45 credit scores 267 Crete-Nishihata, Masashi 399 Crick, Bernard 72, 389, 408 criminal justice 259 Cronologics 319 Cross, Tim 375 Crossley, Rob 388 crowdocracy see Wiki Democracy Crowdpac 417 crowds, wisdom of see wisdom of crowds crowdsourcing 244 cryptography 182–4 Cukier, Kenneth 387, 388, 395, 397, 403, 427, 433 data 62, 65 forgetting versus remembering 137 cultural oppression 273 OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 496 Index Dabate, Connie and Richard 134–5 Dahir, Abdi Latif 405 Dahl, Robert A. 91, 390, 391, 411, 430 Daily Stormer 236 D’Ancona, Matthew 239, 412, 415 Dandeker, Christopher 391 Darknessbot 234 data as capital 317 increasingly quantified society 61–7 data-based injustice 282 Data Deal 66, 336–40, 358 Data Democracy 212, 246–50, 254, 348 datafication 62–7 data storage digitization 62 nanotechnology 56 usufructuary rights 330 data unions 340 Dayen, David 427 Dean, Sarah 402 Decentralised Autonomous Organisations (DAOs) 47 Deep Knowledge Ventures 31, 251 Defense Advanced Research Projects Agency (DARPA) 47, 178 De Filippi, Primavera 120, 378, 392, 393, 394 Delaney, Kevin J. 425, 430 delegation AI Democracy 252 Direct Democracy 242 Deleuze, Gilles 395 Delft University 56 Deliberative Democracy 212, 227–39, 254, 348 democracy 3, 10, 23–4, 346, 359–60 after the internet 219–21 AI Democracy 212, 213, 250–4, 348 arguments for 222–6 classical 214–16, 254 competitive elitism 217–19, 221, 240, 242, 253, 254 concept 74–6 conceptual analysis 81, 82, 84–5 contextual analysis 84–5 Data Democracy 212, 246–50, 254, 348 Deliberative Democracy 212, 227–39, 254, 348 Direct Democracy 212, 239–43, 254, 348 dream of 211–26 epistemic superiority 223–4, 234 in the future 227–54 liberal 216–17, 246, 254 and liberty 207–8, 222, 225, 249 liquid 242 nature of 213 normative analysis 84–5 representative 218, 240, 248 stability 240 story of 213–21 supercharged state, power of the 348 Wiki Democracy 212, 243–6, 254, 348 DemocracyOS 242, 415 Democratic Party (US) 229 desert, justice as 260–1 Desert Wolf 404 Desrosières, Alain 369, 370 Devlin, Patrick 202, 203, 204, 407, 408 Diamandis, Peter H. 374, 435 Dickens, Charles 211 dictatorship 71 Digital Confederalism 193, 205–6, 341 structural regulation 357, 358 digital disrespect 276 digital dissent 179–84 digital filtering see filtering digital law 100–14 Digital Liberalism 205 Digital Libertarianism 205 digital liberty 205–7 digital lifeworld algorithmic injustice 279, 285, 290, 292–4 code’s empire 97 democracy 212–13, 222, 227–54 OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Index distributive justice 266, 269 force 100–1, 103, 107–8, 113, 116, 118–19, 121 freedom and the tech firm 188–90, 193–4, 196, 198, 200, 208 increasingly capable systems 29–41 increasingly integrated technology 42–60 increasingly quantified society 61–8 individual responsibility 346–7 justice in recognition 276–8 liberty 168–72, 180, 183, 185, 187 limits 360–1 perception-control 146–52 post-politics 362, 366 power 98–9, 345–6 property 314–17, 320, 322–3, 328–31, 334–6, 340–1 public and private power 154, 156, 158, 160 regulation 354, 357 scrutiny 123, 127–41 social justice 258–9 technological unemployment 295, 304, 306, 311 thinking like a theorist 69–86 Digital Millennium Copyright Act 1988 430 Digital Moralism 206 Digital Paternalism 198, 199, 206 digital ranking 276–8 Digital Republicanism 206–7, 347 structural regulation 357 Digital Rights Management (DRM) technology 96, 102, 105, 172, 333 digital storage 129 digitization 62 of force 100, 101–14 Direct Democracy 212, 239–43, 254, 348 disabilities, people with digital liberation 169 as victims of violence crimes 273 Discord 236 discrimination 497 algorithmic 281–2 rule-based injustice 284, 287–8 disrespect, digital 276 dissent, digital 179–84 distributed computing see smart devices distributive justice 257–70, 274, 278 Data Deal 337 Private Property Paradigm 326 Divine Rule 349 DNA 64, 362 Dodge, Martin 391 Domesday Book 16–17, 369 dominant goods 154 Domingos, Pedro 373, 374, 410, 417, 432 computing power, growth in 38 data unions 340 machine learning 34–5 Drahos, Peter 431 Dredge, Stuart 384, 385 driverless vehicles see self-driving vehicles drones force 106 hacking 183 increasingly integrated technology 54, 55 productive technologies 316 sharing economy 335 totalitarianism 179 utility analogy 158 Dryzek, John S. 368 Dunn, John 408, 409 Durkheim, Émile 61 Dvorsky, George 384 Dworkin, Gerald 171, 352, 401, 402, 432 Dwoskin, Elizabeth 433 Dwyer, Paula 428 Ebay 102 Economist 378, 379, 380, 381, 397, 422 Edelman, Benjamin 423 e-Democracia Wikilegis 244 Edwards, Cory 371 OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 498 Index egalitarianism 259, 261–5 egalitarian plateau 259 e-government 220 Egypt 19, 183 Eisenstein, Elizabeth 62, 387 Ekbia, Hamid R. 431 Electrick spray paint 51 Electronic Frontier Foundation 406 Eliot, T.


pages: 327 words: 103,336

Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts

active measures, affirmative action, Albert Einstein, Amazon Mechanical Turk, Bear Stearns, Black Swan, business cycle, butterfly effect, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, coherent worldview, collapse of Lehman Brothers, complexity theory, correlation does not imply causation, crowdsourcing, death of newspapers, discovery of DNA, East Village, easy for humans, difficult for computers, edge city, en.wikipedia.org, Erik Brynjolfsson, framing effect, Future Shock, Geoffrey West, Santa Fe Institute, George Santayana, happiness index / gross national happiness, Herman Kahn, high batting average, hindsight bias, illegal immigration, industrial cluster, interest rate swap, invention of the printing press, invention of the telescope, invisible hand, Isaac Newton, Jane Jacobs, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, lake wobegon effect, Laplace demon, Long Term Capital Management, loss aversion, medical malpractice, meta-analysis, Milgram experiment, natural language processing, Netflix Prize, Network effects, oil shock, packet switching, pattern recognition, performance metric, phenotype, Pierre-Simon Laplace, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, social intelligence, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, tacit knowledge, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, Tragedy of the Commons, ultimatum game, urban planning, Vincenzo Peruggia: Mona Lisa, Watson beat the top human players on Jeopardy!, X Prize

Arguably, in fact, the advertising world has more data than it knows what to do with. No, the real problem is that what advertisers want to know is whether their advertising is causing increased sales; yet almost always what they measure is the correlation between the two. In theory, of course, everyone “knows” that correlation and causation are different, but it’s so easy to get the two mixed up in practice that we do it all the time. If we go on a diet and then subsequently lose weight, it’s all too tempting to conclude that the diet caused the weight loss. Yet often when people go on diets, they change other aspects of their lives as well—like exercising more or sleeping more or simply paying more attention to what they’re eating.

But as with the diet, it is the advertising effort on which the business focuses its attention; thus if sales or some other metric of interest subsequently increases, it’s tempting to conclude that it was the advertising, and not something else, that caused the increase.17 Differentiating correlation from causation can be extremely tricky in general. But one simple solution, at least in principle, is to run an experiment in which the “treatment”—whether the diet or the ad campaign—is applied in some cases and not in others. If the effect of interest (weight loss, increased sales, etc.) happens significantly more in the presence of the treatment than it does in the “control” group, we can conclude that it is in fact causing the effect.

Part of the problem is also that social scientists, like everyone else, participate in social life and so feel as if they can understand why people do what they do simply by thinking about it. It is not surprising, therefore, that many social scientific explanations suffer from the same weaknesses—ex post facto assertions of rationality, representative individuals, special people, and correlation substituting for causation—that pervade our commonsense explanations as well. MEASURING THE UNMEASURABLE One response to this problem, as Lazarsfeld’s colleague Samuel Stouffer noted more than sixty years ago, is for sociologists to depend less on their common sense, not more, and instead try to cultivate uncommon sense.10 But getting away from commonsense reasoning in sociology is easier said than done.


pages: 245 words: 64,288

Robots Will Steal Your Job, But That's OK: How to Survive the Economic Collapse and Be Happy by Pistono, Federico

3D printing, Albert Einstein, autonomous vehicles, bioinformatics, Buckminster Fuller, cloud computing, computer vision, correlation does not imply causation, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Firefox, future of work, gamification, George Santayana, global village, Google Chrome, happiness index / gross national happiness, hedonic treadmill, illegal immigration, income inequality, information retrieval, Internet of things, invention of the printing press, Jeff Hawkins, jimmy wales, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, Kiva Systems, knowledge worker, labor-force participation, Lao Tzu, Law of Accelerating Returns, life extension, Loebner Prize, longitudinal study, means of production, Narrative Science, natural language processing, new economy, Occupy movement, patent troll, pattern recognition, peak oil, post scarcity, QR code, race to the bottom, Ray Kurzweil, recommendation engine, RFID, Rodney Brooks, selection bias, self-driving car, slashdot, smart cities, software as a service, software is eating the world, speech recognition, Steven Pinker, strong AI, technological singularity, Turing test, Vernor Vinge, warehouse automation, warehouse robotics, women in the workforce

* * * Figure 13.2: Comparing ‘happiness’ and ‘growth’ over time with n-grams. Courtesy of Google. * * * We can see in Figure 13.2 how ‘happiness’ and ‘growth’, between 1800 and 2008 have a negative correlation: as ‘growth’ rises, ‘happiness’ declines. Around 1830, authors started to talk more about growth than happiness. Again, to be fair, correlation does not imply causation, and the mere fact of writing about something does not tell you the whole story. This data only shows the occurrences of such words in books, not their context, nor their meaning. Authors could well have been talking about the ‘loss of happiness’, or something even more subtle. But it does show that the interest in growth has been, well, growing, whereas writers cared less to talk about being happy.

I took the specific term ‘economic growth’, to rule out other possible disturbances in context. ‘Happiness’ declines from 1950 to 1995, while ‘economic growth’ and ‘GDP’ rise. After that we observe the reverse effect: both ‘GDP’ and ‘economic growth’ fall, while happiness increases considerably. Again, correlation does not mean causation, but it surely is remarkable what this data shows. For more than half a century, our culture has been fuelling the idea that the pursuit of growth, work, and economic expansion should be one of our primary goals in life, if not the highest of all. But that assumption is being challenged and it is slowly beginning to crumble.

While I enjoy picking on the self-help idiocy wave that has invaded the United States and the UK these last five years, there are some suggestions that might actually help you, if you approach them with a bit of scientific rigour. I imagine you must be pretty tired of reading about things that do not work, scientific analyses with no clear distinction between correlation and causation, and plain old common sense masqueraded as hidden truth. How about some practical suggestions, things that you can apply in your daily life, that you would not already know? You know my position regarding self-help. I think it is mostly a pseudoscientific scam that greedy people play on the desperate and the gullible.


pages: 204 words: 58,565

Keeping Up With the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport, Jinho Kim

Black-Scholes formula, business intelligence, business process, call centre, computer age, correlation coefficient, correlation does not imply causation, Credit Default Swap, data science, en.wikipedia.org, feminist movement, Florence Nightingale: pie chart, forensic accounting, global supply chain, Gregor Mendel, Hans Rosling, hypertext link, invention of the telescope, inventory management, Jeff Bezos, Johannes Kepler, longitudinal study, margin call, Moneyball by Michael Lewis explains big data, Myron Scholes, Netflix Prize, p-value, performance metric, publish or perish, quantitative hedge fund, random walk, Renaissance Technologies, Robert Shiller, self-driving car, sentiment analysis, six sigma, Skype, statistical model, supply-chain management, text mining, the scientific method, Thomas Davenport

Correlation = +1 (Perfect positive correlation, meaning that both variables always move in the same direction together) Correlation = 0 (No relationship between the variables) Correlation = −1 (Perfect negative correlation, meaning that as one variable goes up, the other always trends downward) Correlation does not imply causation. Correlation is a necessary but insufficient condition for casual conclusions. Dependent variable: The variable whose value is unknown that you would like to predict or explain. For example, if you wish to predict the quality of a vintage wine using average growing season temperature, harvest rainfall, winter rainfall, and the age of the vintage, the quality of a vintage wine would be the dependent variable.

As we mentioned earlier in describing mad scientist experiments, if you create test and control groups and randomly assign people to them, if there turns out to be a difference in outcomes between the two groups, you can usually attribute it to being caused by the test condition. But if you simply find a statistical relationship between two factors, it’s unlikely to be a causal relationship. You may have heard the phrase, “correlation is not causation,” and it’s important to remember. Cognitive psychologists Christopher Chabris and Daniel Simons suggest a useful technique for checking on the causality issue in their book The Invisible Gorilla and Other Ways Our Intuitions Deceive Us: “When you hear or read about an association between two factors, think about whether people could have been assigned randomly to conditions for one of them.


pages: 877 words: 182,093

Wealth, Poverty and Politics by Thomas Sowell

affirmative action, Alan Greenspan, Albert Einstein, British Empire, Capital in the Twenty-First Century by Thomas Piketty, colonial exploitation, colonial rule, correlation does not imply causation, cotton gin, Deng Xiaoping, desegregation, European colonialism, full employment, Gunnar Myrdal, Herman Kahn, income inequality, income per capita, invention of the sewing machine, invisible hand, low skilled workers, mass immigration, means of production, minimum wage unemployment, New Urbanism, profit motive, rent control, Scramble for Africa, Simon Kuznets, Steve Jobs, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, transatlantic slave trade, transcontinental railway, trickle-down economics, very high income, W. E. B. Du Bois, War on Poverty

Culture also does not lend itself to quantification, as a genetic determinist has complained,29 and therefore cannot produce statistical analyses, such as that showing a high correlation between nations’ IQ scores and their per capita incomes.30 Such correlations may lend an air of scientific precision, but so did earlier correlations between climate and prosperity by a geographic determinist.31 Both sets of correlations are from data taken in an extremely thin slice of time, compared to the many millennia of human history, during which various peoples’ and nations’ relative achievements have changed greatly. Moreover, as statisticians have often pointed out, correlation is not causation— and, as was said years ago: “It is better to be roughly right than precisely wrong.”32 Whether considering cultural, geographic, political or other factors, interactions of these various factors are part of the reason why understanding influences is very different from claiming determinism.

Nevertheless, many people blame statistical inequalities on the institutions where the statistics that convey these inequalities happened to be collected. Others blame some factor with which negative outcomes are correlated— blaming crime on poverty, for example. Statisticians have long warned against confusing correlation with causation, but too often those warnings have been ignored. Even when there is in fact a causal relationship between two things, that by itself does not tell us the direction of causation— that is, whether X caused Y or Y caused X, or whether both were caused by some other factor Z. It is possible that poverty causes crime, but it is also possible that the same set of attitudes and behavior— or the same lack of human capital— that lead to poverty can also lead to crime.

In 1981 and in 1995, for example, the average SAT score of black high school students on the mathematics portion of the test was lower than the average score of either white or Asian high school students. Since black students come from families with lower average incomes than either white or Asian students, this establishes correlation but does not help us determine causation, much less the direction of causation. However, when in 1981 black students from families with incomes of $50,000 or more scored slightly below white students from families with incomes under $6,000, and even further below Asian students with incomes under $6,000,2 clearly the cause of the test score differences was not differences in income.


pages: 654 words: 191,864

Thinking, Fast and Slow by Daniel Kahneman

Albert Einstein, Atul Gawande, availability heuristic, Bayesian statistics, Black Swan, Cass Sunstein, Checklist Manifesto, choice architecture, cognitive bias, complexity theory, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, delayed gratification, demand response, endowment effect, experimental economics, experimental subject, Exxon Valdez, feminist movement, framing effect, hedonic treadmill, hindsight bias, index card, information asymmetry, job satisfaction, John Bogle, John von Neumann, Kenneth Arrow, libertarian paternalism, loss aversion, medical residency, mental accounting, meta-analysis, nudge unit, pattern recognition, Paul Samuelson, precautionary principle, pre–internet, price anchoring, quantitative trading / quantitative finance, random walk, Richard Thaler, risk tolerance, Robert Metcalfe, Ronald Reagan, Shai Danziger, sunk-cost fallacy, Supply of New York City Cabdrivers, The Chicago School, The Wisdom of Crowds, Thomas Bayes, transaction costs, union organizing, Walter Mischel, Yom Kippur War

The control group is expected to improve by regression alone, and the aim of the experiment is to determine whether the treated patients improve more than regression can explain. Incorrect causal interpretations of regression effects are not restricted to readers of the popular press. The statistician Howard Wainer has drawn up a long list of eminent researchers who have made the same mistake—confusing mere correlation with causation. Regression effects are a common source of trouble in research, and experienced scientists develop a healthy fear of the trap of unwarranted causal inference. One of my favorite examples of the errors of intuitive prediction is adapted from Max Bazerman’s excellent text Judgment in Managerial Decision Making: You are the sales forecaster for a department store chain.

income and education: The correlation appears impressive, but I was surprised to learn many years ago from the sociologist Christopher Jencks that if everyone had the same education, the inequality of income (measured by standard deviation) would be reduced only by about 9%. The relevant formula is v (1–r2), where r is the correlation. correlation and regression: This is true when both variables are measured in standard scores—that is, where each score is transformed by removing the mean and dividing the result by the standard deviation. confusing mere correlation with causation: Howard Wainer, “The Most Dangerous Equation,” American Scientist 95 (2007): 249–56. 18: Taming Intuitive Predictions far more moderate: The proof of the standard regression as the optimal solution to the prediction problem assumes that errors are weighted by the squared deviation from the correct value.

Statistical Prediction: A Theoretical Analysis and a Review of the Evidence (Meehl) Clinton, Bill Coelho, Marta coffee mug experiments cognitive busyness cognitive ease; in basic assessments; and illusions of remembering; and illusions of truth; mood and; and writing persuasive messages; WYSIATI (what you see is all there is) and cognitive illusions; confusing experiences with memories; of pundits; of remembering; of skill; of stock-picking skill; of truth; of understanding; of validity Cognitive Reflection Test (CRT) cognitive strain Cohen, David coherence; see also associative coherence Cohn, Beruria coincidence coin-on-the-machine experiment cold-hand experiment Collins, Jim colonoscopies colostomy patients competence, judging of competition neglect complex vs. simple language concentration cogndiv height="0%"> “Conditions for Intuitive Expertise: A Failure to Disagree” (Kahneman and Klein) confidence; bias of, over doubt; overconfidence; WYSIATI (what you see is all there is) and confirmation bias conjunction fallacy conjunctive events, evaluation of “Consequences of Erudite Vernacular Utilized Irrespective of Necessity: Problems with Using Long Words Needlessly” (Oppenheimer) contiguity in time and place control cookie experiment correlation; causation and; illusory; regression and; shared factors and correlation coefficient cost-benefit correlation costs creativity; associative memory and credibility Csikszentmihalyi, Mihaly curriculum team Damasio, Antonio dating question Dawes, Robyn Day Reconstruction Method (DRM) death: causes of; life stories and; organ donation and; reminders of Deaton, Angus decisions, decision making; broad framing in; and choice from description; and choice from experience; emotions and vividness in; expectation principle in; in gambles, see gambles; global impressions and; hindsight bias and; narrow framing in; optimistic bias in; planning fallacy and; poverty and; premortem and; reference points in; regret and; risk and, see risk assessment decision utility decision weights; overweighting; unlikely events and; in utility theory vs. prospect theory; vivid outcomes and; vivid probabilities and decorrelated errors default options denominator neglect depression Detroit/Michigan problem Diener, Ed die roll problem dinnerware problem disclosures disease threats disgust disjunctive events, evaluation of disposition effect DNA evidence dolphins Dosi, Giovanni doubt; bias of confidence over; premortem and; suppression of Duke University Duluth, Minn., bridge in duration neglect duration weighting earthquakes eating eBay Econometrica economics; behavioral; Chicago school of; neuroeconomics; preference reversals and; rational-agent model in economic transactions, fairness in Econs and Humans Edge Edgeworth, Francis education effectiveness of search sets effort; least, law of; in self-control ego depletion electricity electric shocks emotional coherence, see halo effect emotional learning emotions and mood: activities and; affect heuristic; availability biases and; in basic assessments; cognitive ease and; in decision making; in framing; mood heuristic for happiness; negative, measuring; and outcomes produced by action vs. inaction; paraplegics and; perception of; substitution of question on; in vivid outcomes; in vivid probabilities; weather and; work and employers, fairness rules and endangered species endowment effect; and thinking like a trader energy, mental engagement Enquiry Concerning Human Understanding, An (Hume) entrepreneurs; competition neglect by Epley, Nick Epstein, Seymour equal-weighting schemes Erev, Ido evaluability hypothesis evaluations: joint; joint vs. single; single evidence: one-sided; of witnesses executive control expectation principle expectations expected utility theory, see utility theory experienced utility experience sampling experiencing self; well-being of; see also well-being expert intuition; evaluating; illusions of validity of; overconfidence and; as recognition; risk assessment and; vs. statistical predictions; trust in expertise, see skill Expert Political Judgment: How Good Is It?


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How to Read a Paper: The Basics of Evidence-Based Medicine by Trisha Greenhalgh

call centre, complexity theory, conceptual framework, correlation coefficient, correlation does not imply causation, deskilling, knowledge worker, longitudinal study, meta-analysis, microbiome, New Journalism, p-value, personalized medicine, placebo effect, publication bias, randomized controlled trial, selection bias, the scientific method

Was systematic bias avoided or minimised? Was assessment ‘blind’? Were preliminary statistical questions addressed? Summing up References Chapter 5: Statistics for the non-statistician How can non-statisticians evaluate statistical tests? Have the authors set the scene correctly? Paired data, tails and outliers Correlation, regression and causation Probability and confidence The bottom line Summary References Chapter 6: Papers that report trials of drug treatments and other simple interventions ‘Evidence’ and marketing Making decisions about therapy Surrogate endpoints What information to expect in a paper describing a randomised controlled trial: the CONSORT statement Getting worthwhile evidence out of a pharmaceutical representative References Chapter 7: Papers that report trials of complex interventions Complex interventions Ten questions to ask about a paper describing a complex intervention References Chapter 8: Papers that report diagnostic or screening tests Ten men in the dock Validating diagnostic tests against a gold standard Ten questions to ask about a paper that claims to validate a diagnostic or screening test Likelihood ratios Clinical prediction rules References Chapter 9: Papers that summarise other papers (systematic reviews and meta-analyses) When is a review systematic?

Some data, however, cannot be transformed into a smooth pattern, and the significance of this is discussed subsequently. Deciding whether data are normally distributed is not an academic exercise, because it will determine what type of statistical tests to use. For example, linear regression (see section ‘Correlation, regression and causation’) will give misleading results unless the points on the scatter graph form a particular distribution about the regression line—that is, the residuals (the perpendicular distance from each point to the line) should themselves be normally distributed. Transforming data to achieve a normal distribution (if this is indeed achievable) is not cheating.

Some weeks later, I met the technician who had analysed the specimens and he asked ‘Whatever happened to that chap with acromegaly?’ Statistically correcting for outliers (e.g. to modify their effect on the overall result) is quite a sophisticated statistical manoeuvre. If you are interested, try the relevant section in your favourite statistics textbook. Correlation, regression and causation Has correlation been distinguished from regression, and has the correlation coefficient (‘r-value’) been calculated and interpreted correctly? For many non-statisticians, the terms correlation and regression are synonymous, and refer vaguely to a mental image of a scatter graph with dots sprinkled messily along a diagonal line sprouting from the intercept of the axes.


pages: 304 words: 82,395

Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schonberger, Kenneth Cukier

23andMe, Affordable Care Act / Obamacare, airport security, barriers to entry, Berlin Wall, big data - Walmart - Pop Tarts, Black Swan, book scanning, business intelligence, business process, call centre, cloud computing, computer age, correlation does not imply causation, dark matter, data science, double entry bookkeeping, Eratosthenes, Erik Brynjolfsson, game design, IBM and the Holocaust, index card, informal economy, intangible asset, Internet of things, invention of the printing press, Jeff Bezos, Joi Ito, lifelogging, Louis Pasteur, Marc Benioff, Mark Zuckerberg, Max Levchin, Menlo Park, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, obamacare, optical character recognition, PageRank, paypal mafia, performance metric, Peter Thiel, post-materialism, random walk, recommendation engine, Salesforce, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, smart grid, smart meter, social graph, speech recognition, Steve Jobs, Steven Levy, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!

So the quarantine applies only to the individual Internet users whose searches were most highly correlated with having the flu. Here we have the data on whom to pick up. Federal agents, armed with lists of Internet Protocol addresses and mobile GPS information, herd the individual web searchers into quarantine centers. But as reasonable as this scenario might sound to some, it is just plain wrong. Correlations do not imply causation. These people may or may not have the flu. They’d have to be tested. They’d be prisoners of a prediction, but more important, they’d be victims of a view of data that lacks an appreciation for what the information actually means. The point of the actual Google Flu Trends study is that certain search terms are correlated with the outbreak—but the correlation may exist because of circumstances like healthy co-workers hearing sneezes in the office and going online to learn how to protect themselves, not because the searchers are ill themselves.

At the same time, Flowers and his kids continually tested their system with veteran inspectors, drawing on their experience to make the system perform better. Yet the most important reason for the program’s success was that it dispensed with a reliance on causation in favor of correlation. “I am not interested in causation except as it speaks to action,” explains Flowers. “Causation is for other people, and frankly it is very dicey when you start talking about causation. I don’t think there is any cause whatsoever between the day that someone files a foreclosure proceeding against a property and whether or not that place has a historic risk for a structural fire.


pages: 278 words: 70,416

Super Thinking: The Big Book of Mental Models by Gabriel Weinberg, Lauren McCann

affirmative action, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, anti-pattern, Anton Chekhov, autonomous vehicles, bank run, barriers to entry, Bayesian statistics, Bernie Madoff, Bernie Sanders, Black Swan, Broken windows theory, business process, butterfly effect, Cal Newport, Clayton Christensen, cognitive dissonance, commoditize, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, David Attenborough, delayed gratification, deliberate practice, discounted cash flows, disruptive innovation, Donald Trump, Douglas Hofstadter, Edward Lorenz: Chaos theory, Edward Snowden, effective altruism, Elon Musk, en.wikipedia.org, experimental subject, fear of failure, feminist movement, Filter Bubble, framing effect, friendly fire, fundamental attribution error, Goodhart's law, Gödel, Escher, Bach, heat death of the universe, hindsight bias, housing crisis, Ignaz Semmelweis: hand washing, illegal immigration, income inequality, information asymmetry, Isaac Newton, Jeff Bezos, John Nash: game theory, lateral thinking, loss aversion, Louis Pasteur, Lyft, mail merge, Mark Zuckerberg, meta-analysis, Metcalfe’s law, Milgram experiment, minimum viable product, moral hazard, mutually assured destruction, Nash equilibrium, Network effects, nuclear winter, offshore financial centre, p-value, Parkinson's law, Paul Graham, peak oil, Peter Thiel, phenotype, Pierre-Simon Laplace, placebo effect, Potemkin village, precautionary principle, prediction markets, premature optimization, price anchoring, principal–agent problem, publication bias, recommendation engine, remote working, replication crisis, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Ronald Reagan, Salesforce, school choice, Schrödinger's Cat, selection bias, Shai Danziger, side project, Silicon Valley, Silicon Valley startup, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, sunk-cost fallacy, survivorship bias, The future is already here, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Tragedy of the Commons, transaction costs, uber lyft, ultimatum game, uranium enrichment, urban planning, Vilfredo Pareto, warehouse robotics, When a measure becomes a target, wikimedia commons

You may have heard anecdotes about people who happened to get cold and flu symptoms around the time that they got the flu vaccine and blame their illness on the vaccine. Just because two events happened in succession, or are correlated, doesn’t mean that the first actually caused the second. Statisticians use the phrase correlation does not imply causation to describe this fallacy. What is often overlooked when this fallacy arises is a confounding factor, a third, possibly non-obvious factor that influences both the assumed cause and the observed effect, confounding the ability to draw a correct conclusion. In the case of the flu vaccine, the cold and flu season is that confounding factor.

It’s easier than ever to test the correlation between all sorts of information, so many spurious correlations are bound to be discovered. In fact, there is a hilarious site (and book) called Spurious Correlations, chock-full of these silly results. The graph below shows one such correlation, between cheese consumption and deaths due to bedsheet tanglings. Correlation Does Not Imply Causation One time when Lauren was in high school, she started feeling like a cold was coming on, and her dad told her to drink plenty of fluids to help her get better. She proceeded to drink half a case of raspberry Snapple that day, and, surprisingly, the next day she felt a lot better!

There are certainly lots of pitfalls to watch out for, but we hope you also take away the fact that research and data are more useful for navigating uncertainty than hunches and opinions. KEY TAKEAWAYS Avoid succumbing to the gambler’s fallacy or the base rate fallacy. Anecdotal evidence and correlations you see in data are good hypothesis generators, but correlation does not imply causation—you still need to rely on well-designed experiments to draw strong conclusions. Look for tried-and-true experimental designs, such as randomized controlled experiments or A/B testing, that show statistical significance. The normal distribution is particularly useful in experimental analysis due to the central limit theorem.


pages: 698 words: 198,203

The Stuff of Thought: Language as a Window Into Human Nature by Steven Pinker

airport security, Albert Einstein, Bob Geldof, colonial rule, conceptual framework, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Brooks, Douglas Hofstadter, en.wikipedia.org, experimental subject, fudge factor, George Santayana, Laplace demon, loss aversion, luminiferous ether, Norman Mailer, Richard Feynman, Ronald Reagan, Sapir-Whorf hypothesis, science of happiness, social intelligence, speech recognition, stem cell, Steven Pinker, Thomas Bayes, Thorstein Veblen, traffic fines, urban renewal, Yogi Berra

The stock of words in a language reflects the kinds of things its speakers deal with in their lives and hence think about. This, of course, is the obvious non-Whorfian interpretation of the Eskimo-snow factoid. The Whorfian interpretation is a classic example of the fallacy of confusing correlation with causation. In the case of varieties of snow and words for snow, not only did the snow come first, but when people change their attention to snow, they change their words as a result. That’s how meteorologists, skiers, and New Englanders coin new expressions for the stuff, whether in circumlocutions (wet snow, sticky snow) or in neologisms (hardpack, powder, dusting, flurries).

Gordon concluded that the lack of precise number thoughts among the Pirahã is caused by their lack of precise number words—the “rare and perhaps unique case for strong linguistic determinism.” But as the cognitive scientist Daniel Casasanto put it, this is a case of “crying Whorf ”: it depends on a dubious leap from correlation to causation.94 It can’t be a coincidence that the Pirahã language just happens to lack big number words (unlike the English language) and the Pirahã speakers just happen to hunt and gather in remote stone-age villages (unlike English speakers). A more plausible interpretation is that the lifestyle, history, and culture of a technologically undeveloped hunter-gatherer people will cause it to lack both number words and numerical reasoning.

It’s not terribly different from what happens when a dog is conditioned to anticipate food when a bell is rung, or a pigeon learns to peck a key in the expectation of food. The story that began the chapter, about the two alarms that go off in succession, raises an obvious problem for the theory. People understand (even if they don’t always apply) the principle that correlation does not imply causation. The rooster’s cock-a-doodle-doo does not cause the sun to rise, thunder doesn’t cause forest fires, and the flashing lights on the top of a printer don’t cause it to spit out a document. These are perceived to be epiphenomena: byproducts of the real causes. I called Hume’s theory “offhand” because he didn’t consistently embrace it himself.


pages: 249 words: 81,217

The Art of Rest: How to Find Respite in the Modern Age by Claudia Hammond

Anton Chekhov, conceptual framework, correlation does not imply causation, Desert Island Discs, Donald Trump, El Camino Real, iterative process, Kickstarter, lifelogging, longitudinal study, Menlo Park, meta-analysis, Milgram experiment, moral panic, Stephen Hawking, The Spirit Level, The Theory of the Leisure Class by Thorstein Veblen, The Wisdom of Crowds, theory of mind, Thorstein Veblen

It won’t surprise you to learn that people who are unemployed or stuck at home because they’re unwell tend to watch more TV on average.15 It’s cheap, ever-changing, doesn’t require physical fitness and can provide hours of distraction. Those same people also have lower levels of well-being than people in good health or with jobs. So we are left with a perennial research issue – correlation versus causation. We don’t know which came first – the unhappiness or the TV watching. Staying in all day watching television might well isolate people and make them feel worse. Alternatively, they may already be feeling unhappy and are using TV to cope, like the lonely people we heard about earlier who binge watch box sets.

Perhaps these people were so poor they couldn’t afford a television or so busy working and caring for others that they never had time to rest and watch it, in which case, of course, it was the lack of any free time and the overwhelming stress of their lives making them unhappy rather than the lack of an hour’s television watching. To get around the correlation versus causation issue, researchers in the US examined data from 50,000 nurses, who were followed over a ten-year period. Did long hours spent in front of the television precede depression several years later? For many of the nurses it did. And the reason? Watching lots of TV meant they did less exercise, and the authors think it’s that rather than anything about watching TV per se that was the main problem.17 It is obvious that watching a lot of TV is not good for us physically because it generally involves a lot of sitting down.


pages: 251 words: 80,831

Super Founders: What Data Reveals About Billion-Dollar Startups by Ali Tamaseb

"side hustle", 23andMe, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Anne Wojcicki, barriers to entry, Ben Horowitz, bitcoin, business intelligence, buy and hold, Chris Wanstrath, clean water, cloud computing, coronavirus, corporate governance, correlation does not imply causation, COVID-19, cryptocurrency, data science, discounted cash flows, diversified portfolio, Elon Musk, Fairchild Semiconductor, game design, gig economy, high net worth, hiring and firing, index fund, Internet Archive, Jeff Bezos, Kickstarter, late fees, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Max Levchin, Mitch Kapor, natural language processing, Network effects, nuclear winter, PageRank, PalmPilot, Paul Buchheit, Paul Graham, peer-to-peer lending, Peter Thiel, QR code, Recombinant DNA, remote working, ride hailing / ride sharing, robotic process automation, rolodex, Ruby on Rails, Salesforce, Sam Altman, Sand Hill Road, self-driving car, shareholder value, sharing economy, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, SoftBank, software as a service, software is eating the world, sovereign wealth fund, Startup school, Steve Jobs, Steve Wozniak, survivorship bias, TaskRabbit, telepresence, the payments system, Tony Hsieh, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, web application, WeWork, Y Combinator

. | Business—Data processing. Classification: LCC HD62.5 .T3554 2021 | DDC 658.1/1—dc23 LC record available at https://lccn.loc.gov/2020044864 ISBNs: 978-1-5417-6842-0 (hardcover), 978-1-5417-6841-3 (ebook) E3-20210420-JV-NF-ORI CONTENTS Cover Title Page Copyright Introduction Correlation Is Not Causation: A Note on Methods and Statistics PART ONE: THE FOUNDERS 1 Myths Around Founders’ Backgrounds Founding a Billion-Dollar Startup at Age Twenty-One: INTERVIEW WITH HENRIQUE DUBUGRAS OF BREX 2 Myths Around Founders’ Education A Professor Who Built Multiple Billion-Dollar Startups: INTERVIEW WITH ARIE BELLDEGRUN OF KITE PHARMA AND ALLOGENE 3 Myths Around Founders’ Work Experience Founders Who Built a $2 Billion Cancer Company Without Any Medical Background: INTERVIEW WITH NAT TURNER OF FLATIRON HEALTH 4 The Super Founder A Founder Who Met Success on the Second Try: INTERVIEW WITH MAX MULLEN OF INSTACART PART TWO: THE COMPANY 5 The Origin Story A Billion-Dollar Startup That Originated at a Large Tech Company: INTERVIEW WITH NEHA NARKHEDE OF CONFLUENT 6 Pivots 7 What and Where?

It turns out that you could look more like a Super Founder than you think. One final note: while doing this research I could not help but notice the lack of diversity among these founding teams. Inspired by Ben Horowitz, I will donate proceeds from this book to nonprofits and charitable causes that help with upward social mobility and diversity. CORRELATION IS NOT CAUSATION A Note on Methods and Statistics The goal of this book is to reduce biases and misconceptions, not to add more. That’s why I am starting it with a note on the methods and statistics that form its backbone. You are going to read a lot of numbers and percentages in this book, and sometimes it’s easy to make the wrong conclusions from the data without having the right context.

—Ron Conway, founder of SV Angel and angel investor in Google, Facebook, Airbnb, and more “Conventional wisdom about what leads to startup success abounds. With a vast dataset, real rigor, and fresh insight, Tamaseb validates some truisms and debunks many others. A must-read for aspiring founders and venture investors!” —Tom Eisenmann, professor, Harvard Business School and author of Why Startups Fail NOTES CORRELATION IS NOT CAUSATION: A NOTE ON METHODS AND STATISTICS 1. Yoav Benjamini and Yosef Hochberg, “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing,” Journal of the Royal Statistical Society: Series B (Methodological) 57, no. 1 (1995): 289–300, doi: 10.1111/j.2517-6161.1995.tb02031.x.


Trading Risk: Enhanced Profitability Through Risk Control by Kenneth L. Grant

backtesting, business cycle, buy and hold, commodity trading advisor, correlation coefficient, correlation does not imply causation, delta neutral, diversification, diversified portfolio, financial engineering, fixed income, frictionless, frictionless market, George Santayana, global macro, implied volatility, interest rate swap, invisible hand, Isaac Newton, John Meriwether, Long Term Capital Management, managed futures, market design, Myron Scholes, performance metric, price mechanism, price stability, risk free rate, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, the scientific method, The Wealth of Nations by Adam Smith, transaction costs, two-sided market, value at risk, volatility arbitrage, yield curve, zero-coupon bond

I was less successful in convincing him that the Beach Boy’s “Pet Sounds” is the most overrated album in the history of popular music and that Brian Eno’s “Here Come the Warm Jets” is the most underrated. “Baby’s on fire, better throw her in the water.” I can’t close this discussion without referencing an old platitude that cautions against confusing “correlation with causation.” While correlation analysis can be extremely useful in understanding patterns, providing insights, and offering clues as to what is driving performance, it is crucial to resist the temptation of reading too much into associated outcomes. Ideally, like all other statistics discussed in this chapter, the calculation of correlations will evoke as many questions as answers.

Final Word on Correlation I caution you, yet again, against reading too much into the implications of the results. Correlation analysis is a very useful descriptive statistic, but it is an imprecise predictive mechanism. As such, I can’t stress strongly enough the age-old adage admonishing us not to confuse correlation with causation. The goal here is to gain insight into those elements of your routine trading program that are most likely to bring you success in your quest for risk-adjusted return and into those that are causing inefficiencies that can at least be managed, if not altogether corrected. Remember that, due to the extraordinary amount of complexity that is involved in the portfolio management process, any change you make to your program designed to address an anomaly uncovered by these types of statistical analyses may very well have implications for other elements of your methodologies that could offset the potential benefits you seek through the change.


pages: 340 words: 94,464

Randomistas: How Radical Researchers Changed Our World by Andrew Leigh

Albert Einstein, Amazon Mechanical Turk, Anton Chekhov, Atul Gawande, basic income, Black Swan, correlation does not imply causation, crowdsourcing, data science, David Brooks, Donald Trump, ending welfare as we know it, Estimating the Reproducibility of Psychological Science, experimental economics, Flynn Effect, germ theory of disease, Ignaz Semmelweis: hand washing, Indoor air pollution, Isaac Newton, It's morning again in America, Kickstarter, longitudinal study, loss aversion, Lyft, Marshall McLuhan, meta-analysis, microcredit, Netflix Prize, nudge unit, offshore financial centre, p-value, placebo effect, price mechanism, publication bias, RAND corporation, randomized controlled trial, recommendation engine, Richard Feynman, ride hailing / ride sharing, Robert Metcalfe, Ronald Reagan, Sheryl Sandberg, statistical model, Steven Pinker, uber lyft, universal basic income, War on Poverty

You might reasonably respond that this is because happiness causes sleep – good-tempered people tend to hit the pillow early. Or you might argue that both happiness and sleep are products of something else – like being in a stable relationship. Either way, an observational study falls prey to the old critique: correlation doesn’t imply causation. Misleading correlations are all around us.32 Ice-cream sales are correlated with shark attacks, but that doesn’t mean you should boycott Mr Whippy. Shoe size is correlated with exam performance, but buying adult shoes for kindergarteners isn’t going to help. Countries with higher chocolate consumption win more Nobel prizes, but chomping Cadbury won’t make you a genius.33 By contrast, a randomised trial uses the power of chance to assign the groups.

U2’s Bono wrote: ‘Give a man a fish, he’ll eat for a day. Give a woman microcredit, she, her husband, her children, and her extended family will eat for a lifetime.’12 Yet it turned out that the bold claims for microcredit were largely based on anecdotes and evaluations that failed to distinguish correlation from causation. By the 2000s, researchers had begun carrying out randomised trials of microcredit programs in Bosnia, Ethiopia, India, Mexico, Morocco and Mongolia. Summarising these six experiments, a team of leading development economists concluded that microcredit had no impact on raising household income, getting children to stay in school, or empowering women.13 Microcredit schemes did provide more financial freedom, and led people to invest more money in their businesses, but it didn’t make them more profitable.


pages: 336 words: 93,672

The Future of the Brain: Essays by the World's Leading Neuroscientists by Gary Marcus, Jeremy Freeman

23andMe, Albert Einstein, backpropagation, bioinformatics, bitcoin, brain emulation, cloud computing, complexity theory, computer age, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data acquisition, data science, Drosophila, epigenetics, global pandemic, Google Glasses, ITER tokamak, iterative process, linked data, mouse model, optical character recognition, pattern recognition, personalized medicine, phenotype, race to the bottom, Richard Feynman, Ronald Reagan, semantic web, speech recognition, stem cell, Steven Pinker, supply-chain management, tacit knowledge, Turing machine, twin studies, web application

This cannot be emphasized enough. The exploding use of opto- and pharmacogenetics methods that delicately, transiently, reversibly, and invasively control defined events in defined cell types at defined times constitute a suite of interventionist tools that allows neuroscience to move from correlation to causation, from observing that this circuit is activated whenever the subject is contemplating a decision to inferring that this circuit is necessary for decision making. Second, the human brain is more than three orders of magnitude larger than the mouse brain—1.4 kg weight versus 0.4 g; a 1-liter volume versus a sugar cube; eighty-six billion nerve cells versus seventy-one million for the entire brain and sixteen billion versus fourteen million nerve cells for the neocortex.

The other major advance fifty years ago was the birth of opto- and pharmaco-genetics, methods that delicately, transiently, reversibly, and invasively control defined events in defined cell types at defined times, initially in a few model organisms—the worm, the fly, and the mouse. Equipped with these tools for perturbing the brain, scientists systematically moved from correlation to causation, from observing that this circuit is activated whenever the subject is contemplating a decision to inferring that this circuit is necessary for decision making or that those neurons mark a particular memory. By the early 2020s, the complete logic of thalamo-cortical circuits could be manipulated, in hindsight a tipping point in our ability to bridge the gap between cortex and theories of its universal and particular functions.


Humble Pi: A Comedy of Maths Errors by Matt Parker

8-hour work day, Affordable Care Act / Obamacare, bitcoin, British Empire, Brownian motion, Chuck Templeton: OpenTable:, collateralized debt obligation, computer age, correlation does not imply causation, crowdsourcing, Donald Trump, Flash crash, forensic accounting, game design, High speed trading, Julian Assange, millennium bug, Minecraft, obamacare, orbital mechanics / astrodynamics, publication bias, Richard Feynman, Richard Feynman: Challenger O-ring, selection bias, Tacoma Narrows Bridge, Therac-25, value at risk, WikiLeaks, Y2K

Multibillionaire investor Warren Buffett is a big fan of non-transitive dice and brought them out when he met also-multibillionaire computer guy Bill Gates. The story goes that Gates’s suspicion was aroused when Buffett insisted he pick his dice first and, upon a closer inspection of the numbers, he in turn insisted Buffett choose first. The link between people who like non-transitive dice and billionaires may be only correlation and not causation. James Grime’s contribution to the non-transitive world was to make it so that his dice have two different possible cycles of non-transitiveness but with only one of them reversing when you double the dice.fn1 By renaming the green dice ‘olive’, the second cycle can be remembered as the alphabetical order of the colours.

Both the number of mobile-phone masts in an area and the number of births depend on how many people live there. I should make it very clear: in the article I explained that the correlation was because of population size. I explained in great detail that this was an exercise in showing that correlation does not mean causation. But it ended up also being an exercise in how people don’t read the article properly before commenting underneath. The correlation was too alluring and people could not help but put forward their own reasons. More than one person suggested that expensive neighbourhoods have fewer masts and young families with loads of kids cannot afford to live there, proving once again that there is no topic that Guardian readers cannot make out to be about house prices.


pages: 576 words: 105,655

Austerity: The History of a Dangerous Idea by Mark Blyth

"Robert Solow", "there is no alternative" (TINA), accounting loophole / creative accounting, Alan Greenspan, balance sheet recession, bank run, banking crisis, Bear Stearns, Black Swan, Bretton Woods, business cycle, buy and hold, capital controls, Carmen Reinhart, Celtic Tiger, central bank independence, centre right, collateralized debt obligation, correlation does not imply causation, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, deindustrialization, disintermediation, diversification, en.wikipedia.org, ending welfare as we know it, Eugene Fama: efficient market hypothesis, eurozone crisis, financial engineering, financial repression, fixed income, floating exchange rates, Fractional reserve banking, full employment, German hyperinflation, Gini coefficient, global reserve currency, Greenspan put, Growth in a Time of Debt, high-speed rail, Hyman Minsky, income inequality, information asymmetry, interest rate swap, invisible hand, Irish property bubble, Joseph Schumpeter, Kenneth Rogoff, liberal capitalism, liquidationism / Banker’s doctrine / the Treasury view, Long Term Capital Management, market bubble, market clearing, Martin Wolf, money market fund, moral hazard, mortgage debt, mortgage tax deduction, Occupy movement, offshore financial centre, paradox of thrift, Philip Mirowski, Phillips curve, Post-Keynesian economics, price stability, quantitative easing, rent-seeking, reserve currency, road to serfdom, savings glut, short selling, structural adjustment programs, tail risk, The Great Moderation, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, Tobin tax, too big to fail, unorthodox policies, value at risk, Washington Consensus, zero-sum game

Rather, in both cases, what was once seen as sustainable suddenly became seen as unsustainable once the possibility of a contagion-led fire sale through the European bond markets was factored into a slow-moving growth crisis. As usual, it’s the perception of risk that matters. And again, just as we saw in the US case, there was no orgy of government spending behind all this. Why, then, keep up the fiction that the bond market crisis is a crisis of spendthrift governments? Confusing Correlation and Causation: Austerity’s Moment in the Sun With yields spiking to unsustainable levels in Greece, Ireland, and Portugal, each country received a bailout from the EU, ECB, and the IMF, as well as bilateral loans, on the condition that it accept and implement an austerity package to right its fiscal ship.

Growth rates and foreign investment both soared.105 Key to all this, as before, was the large expenditure-based cut plus wage moderation and devaluation.106 Stephen Kinsella offers a rather different version of events in his recent study of Ireland’s twin experiments with austerity: in the late 1980s and today in the aftermath of the banking crisis of 2008.107 Kinsella emphasizes that Ireland did have an expansion following a consolidation, as the literature claims, but notes that correlation is not causation in this case. Instead, he notes another correlation; that Ireland’s consolidation “coincided with a period of growth in the international economy, with the presence of fiscal transfers from the European Union, the opening up of the single market and a well-timed devaluation in August 1986.”108 An earlier paper by John Considine and James Duffy makes a similar point, namely, that it’s the boom in British imports—the so-called Lawson boom—that combined with the 1986 devaluation to make the difference.109 This is backed up by a piece by Roberto Perotti, who argues that in the Irish case “the concomitant depreciation of Sterling and the expansion in the UK … boosted Irish exports.”110 Kinsella also notes that the adjustment was considerably eased by an income tax amnesty that raised the equivalent of 2 percent of GDP.111 The part that stands out in Kinsella’s account is, however, something completely absent in other retellings of these events.


pages: 294 words: 77,356

Automating Inequality by Virginia Eubanks

autonomous vehicles, basic income, business process, call centre, cognitive dissonance, collective bargaining, correlation does not imply causation, data science, deindustrialization, disruptive innovation, Donald Trump, Elon Musk, ending welfare as we know it, experimental subject, housing crisis, IBM and the Holocaust, income inequality, job automation, mandatory minimum, Mark Zuckerberg, mass incarceration, minimum wage unemployment, mortgage tax deduction, new economy, New Urbanism, payday loans, performance metric, Ronald Reagan, San Francisco homelessness, self-driving car, statistical model, strikebreaker, underbanked, universal basic income, urban renewal, W. E. B. Du Bois, War on Poverty, warehouse automation, working poor, Works Progress Administration, young professional, zero-sum game

In other words, it searches through all available information to pluck out any variables that vary along with the thing you are trying to measure—which leads to charges that the method is a kind of “data dredging,” or a statistical fishing expedition. For the AFST, the Vaithianathan team tested 287 variables available in Cherna’s data warehouse. The regression knocked out 156 of them, leaving 131 factors that the team believes predict child harm.9 Even if a regression finds factors that predictably rise and fall together, correlation is not causation. In a classic example, shark attacks and ice cream consumption are highly correlated. But that doesn’t mean that eating ice cream makes swimmers too slow to avoid aquatic predators, or that sharks are attracted to soft-serve. There is a third variable that influences both shark attacks and ice cream consumption: summer.

.; Southern Christian Leadership Conference (SCLC) Civil Works Administration (CWA) Civilian Conservation Corps (CCC) Clinton, Bill Cloward, Richard Cohen, Stanley Cohn, Cindy COINTELPRO (the COunter INTELligence PROgram of the FBI) Communism confidentiality. See also privacy Conrad N. Hilton Foundation coordinated entry system (CES) correlation vs. causation county farms. See poorhouses county homes. See poorhouses COWPI. See Indiana, Committee on Welfare Privatization Issues creative economy in Los Angeles in Pennsylvania criminalization and automated decision-making and digital poorhouse and homelessness and poverty and welfare reform Crouch, Suzanne Culhane, Dennis Cullors, Patrisse cultural denial Cunningham, Mary Dalton, Erin Daniels, Mitch Dare, Tim data analytics, regime of mining and right to be forgotten security warehouse decision making automated and big data human and inclusion revolutionary change in and scientific charity tracking of and transparency Declaration of Independence deindustrialization in Indiana in South LA Denton, Nancy A.


pages: 343 words: 101,563

The Uninhabitable Earth: Life After Warming by David Wallace-Wells

"Robert Solow", agricultural Revolution, Albert Einstein, anthropic principle, Asian financial crisis, augmented reality, basic income, Berlin Wall, bitcoin, British Empire, Buckminster Fuller, Burning Man, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, carbon-based life, Chekhov's gun, cognitive bias, computer age, correlation does not imply causation, cryptocurrency, cuban missile crisis, decarbonisation, disinformation, Donald Trump, Dr. Strangelove, effective altruism, Elon Musk, endowment effect, energy transition, everywhere but in the productivity statistics, failed state, fiat currency, global pandemic, global supply chain, income inequality, Intergovernmental Panel on Climate Change (IPCC), invention of agriculture, Joan Didion, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kim Stanley Robinson, labor-force participation, life extension, longitudinal study, Mark Zuckerberg, mass immigration, megacity, megastructure, mutually assured destruction, Naomi Klein, negative emissions, nuclear winter, Paris climate accords, Pearl River Delta, Peter Thiel, plutocrats, postindustrial economy, quantitative easing, Ray Kurzweil, rent-seeking, ride hailing / ride sharing, Sam Altman, Silicon Valley, Skype, South China Sea, South Sea Bubble, Steven Pinker, Stewart Brand, the built environment, The future is already here, the scientific method, Thomas Malthus, too big to fail, universal basic income, University of East Anglia, Whole Earth Catalog, William Langewiesche, Y Combinator

But by laundering all conflict and competition through the market, neoliberalism also proffered a new model of doing business, so to speak, on the world stage—one that didn’t emerge from, or point toward, endless nation-state rivalry. One should not confuse correlation with causation, especially since there was so much tumult coming out of World War II that it is hard to isolate the single cause of just about anything. But the international cooperative order that has since presided, establishing or at least emerging in parallel with relative peace and abundant prosperity, is very neatly historically coincident with the reign of globalization and the empire of financial capital we now group together as neoliberalism. And if one were inclined to confuse correlation with causation, there is a quite intuitive and plausible theory connecting them.


pages: 364 words: 102,926

What the F: What Swearing Reveals About Our Language, Our Brains, and Ourselves by Benjamin K. Bergen

correlation does not imply causation, information retrieval, pre–internet, Ronald Reagan, statistical model, Steven Pinker

Each dot represents the first time the child produced a particular noun; more frequent nouns tended to be learned earlier than less frequent ones. Image reproduced from B. C. Roy et al. (2009), used with permission. Of course, a reasonable person could object to studies like this one. Correlation does not imply causation. So the fact that children tend to learn more frequent words earlier doesn’t entail that frequency is the reason for earlier word learning. Other factors might be in play. For instance, more frequent words are shorter, all things being equal. And children learn shorter words earlier.

The study states that adolescents who reported watching shows and playing games with more profanity in them also reported finding profanity more acceptable and using more profanity themselves. Does this answer the question about frequency? Does this mean that exposure to more profanity leads to more use of profanity? We don’t know, because the study was correlational. It’s not always obvious why correlation doesn’t imply causation, so let me just remind you here. (If this is old hat to you, by all means, skip to the next paragraph.) Here’s a nice example of why you can’t infer causation from correlation.24 Suppose you want to know whether religious faith causes an increase in alcohol consumption. You might try to find an answer by counting the number of bars and the number of churches in each of a large number of US cities.


pages: 377 words: 97,144

Singularity Rising: Surviving and Thriving in a Smarter, Richer, and More Dangerous World by James D. Miller

23andMe, affirmative action, Albert Einstein, artificial general intelligence, Asperger Syndrome, barriers to entry, brain emulation, cloud computing, cognitive bias, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, David Brooks, David Ricardo: comparative advantage, Deng Xiaoping, en.wikipedia.org, feminist movement, Flynn Effect, friendly AI, hive mind, impulse control, indoor plumbing, invention of agriculture, Isaac Newton, John von Neumann, knowledge worker, Long Term Capital Management, low skilled workers, Netflix Prize, neurotypical, Norman Macrae, pattern recognition, Peter Thiel, phenotype, placebo effect, prisoner's dilemma, profit maximization, Ray Kurzweil, recommendation engine, reversible computing, Richard Feynman, Rodney Brooks, Silicon Valley, Singularitarianism, Skype, statistical model, Stephen Hawking, Steve Jobs, supervolcano, tech billionaire, technological singularity, The Coming Technological Singularity, the scientific method, Thomas Malthus, transaction costs, Turing test, twin studies, Vernor Vinge, Von Neumann architecture

To be more precise: intelligence researchers disagree over how much of the variation in people’s IQs is caused by genetics, with estimates ranging from about 50 to 80 percent.162 Researchers don’t agree on the relative importance of genetics in determining IQ because of the challenge of separating correlation from causation. To understand this difficulty, suppose we know that parents who read a lot to their children tend to have children with high IQs. This correlation might occur because reading to a child increases her IQ. But here are some other possible causes, and if any one of them is the correct explanation, reading will do absolutely nothing to boost a child’s intelligence: •The higher a parent’s IQ, the more she enjoys reading to her child, and so the more she will read to her child.

A child’s working memory has been found to be a key predictor of his success in kindergarten as measured by teacher evaluations, perhaps indicating that parents should provide brain training to their toddlers.279 Of course, the relationship between these two indicators might be due merely to correlation, not causation, and so using brain fitness software to improve a four-year-old’s working memory might not help him in kindergarten. If computer brain training proved effective, educators could continually improve it using massive data analysis. Brain-training programs could easily keep track of students’ performances.


Work in the Future The Automation Revolution-Palgrave MacMillan (2019) by Robert Skidelsky Nan Craig

3D printing, Airbnb, algorithmic trading, Alvin Toffler, Amazon Web Services, anti-work, antiwork, artificial general intelligence, autonomous vehicles, basic income, business cycle, cloud computing, collective bargaining, Computing Machinery and Intelligence, correlation does not imply causation, creative destruction, data is the new oil, data science, David Graeber, David Ricardo: comparative advantage, deindustrialization, deskilling, disintermediation, do what you love, Donald Trump, Erik Brynjolfsson, feminist movement, Frederick Winslow Taylor, future of work, Future Shock, gig economy, global supply chain, income inequality, independent contractor, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, job polarisation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, off grid, pattern recognition, post-work, Ronald Coase, scientific management, Second Machine Age, self-driving car, sharing economy, SoftBank, Steve Jobs, strong AI, tacit knowledge, technoutopianism, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor

And even more importantly, even if that prediction was wrong initially, that prediction ends up being correct if enough predictive algorithms agree that “people like them” don’t look likely to pay back loans, and they are systematically shut out of the banking system. In this sense predictions become truth, and correlations become causations. There are plenty of real examples where this touches quite demonstrably on ethics and the public good, but I’ll indulge in a fictional example taken from an episode of Star Trek: Voyager’s seventh season, called “Critical Care.” The Voyager doctor, which is an AI, runs from a holographic emitter, which is stolen by an alien.


Bulletproof Problem Solving by Charles Conn, Robert McLean

active transport: walking or cycling, Airbnb, Amazon Mechanical Turk, asset allocation, availability heuristic, Bayesian statistics, Big Tech, Black Swan, blockchain, business process, call centre, carbon footprint, cloud computing, correlation does not imply causation, Credit Default Swap, crowdsourcing, David Brooks, Donald Trump, Elon Musk, endowment effect, fail fast, future of work, Garrett Hardin, Hyperloop, Innovator's Dilemma, inventory management, iterative process, loss aversion, megaproject, meta-analysis, Nate Silver, nudge unit, Occam's razor, pattern recognition, pets.com, prediction markets, principal–agent problem, RAND corporation, randomized controlled trial, risk tolerance, Silicon Valley, smart contracts, stem cell, sunk-cost fallacy, the rule of 72, the scientific method, The Signal and the Noise by Nate Silver, time value of money, Tragedy of the Commons, transfer pricing, Vilfredo Pareto, walkable city, WikiLeaks

The heat map that results shows the neighborhoods with the highest level of risk. As a first cut, it suggests exploring the issue further is warranted, even though the correlations aren't especially high between particulate matter and hospital admissions for yearlong data. And as we know, correlations do not prove causation; there could be an underlying factor causing both PM 2.5 hotspots and asthma hospital admissions. Experiments, more granular data analysis, and large‐scale models are the next step for this work. EXHIBIT 6.2 Source: Q. Di et al., “Air Pollution and Mortality in the Medicare Population,” New England Journal of Medicine 376 (June 29, 2017), 2513–2522.

This example is just a simple one to show how regression analysis can help you begin to understand the drivers of your problem, and perhaps to craft strategies for positive intervention at the city level. As useful as regression is in exploring our understanding, there are some pitfalls to consider: Be careful with correlation and causation. Walkable cities seem to almost always have far lower obesity rates than less walkable cities. However, we have no way of knowing from statistics alone whether city walkability is the true cause of lower obesity. Perhaps walkable cities are more expensive to live in and the real driver is higher socioeconomic status.


pages: 515 words: 142,354

The Euro: How a Common Currency Threatens the Future of Europe by Joseph E. Stiglitz, Alex Hyde-White

"there is no alternative" (TINA), Alan Greenspan, bank run, banking crisis, barriers to entry, battle of ideas, Berlin Wall, Bretton Woods, business cycle, buy and hold, capital controls, Carmen Reinhart, cashless society, central bank independence, centre right, cognitive dissonance, collapse of Lehman Brothers, collective bargaining, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, currency peg, dark matter, David Ricardo: comparative advantage, disintermediation, diversified portfolio, eurozone crisis, Fall of the Berlin Wall, fiat currency, financial innovation, full employment, George Akerlof, Gini coefficient, global supply chain, Growth in a Time of Debt, housing crisis, income inequality, incomplete markets, inflation targeting, information asymmetry, investor state dispute settlement, invisible hand, Kenneth Arrow, Kenneth Rogoff, knowledge economy, light touch regulation, manufacturing employment, market bubble, market friction, market fundamentalism, Martin Wolf, Mexican peso crisis / tequila crisis, money market fund, moral hazard, mortgage debt, neoliberal agenda, new economy, open economy, paradox of thrift, pension reform, pensions crisis, price stability, profit maximization, purchasing power parity, quantitative easing, race to the bottom, risk-adjusted returns, Robert Shiller, Ronald Reagan, Savings and loan crisis, savings glut, secular stagnation, Silicon Valley, sovereign wealth fund, the payments system, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, transfer pricing, trickle-down economics, Washington Consensus, working-age population

True—but that is precisely what is happening now, as Troika policies have lowered Greek incomes by more than a quarter. More relevant, Greece would likely not have borrowed in German currency, precisely because it (and presumably its lenders) should have been aware of the risk that that entailed.30 CORRELATION AND CAUSATION The poor performance of the eurozone, both absolutely and relative to others, might, of course, be due to some factor other than the euro. And there have been changes in the global economy that have affected the eurozone and, more particularly, one group of countries within the eurozone relative to others.

The notion that there could be expansionary contractions was a chimera. A series of papers showed major flaws in their analysis.57 The IMF, which had supported austerity-style policies in the past, in fact reversed itself. It pointed out that when governments contract spending, the economy contracts.58 The big flaw in the pro-austerity study was confusing correlation with causation. There were a few countries, small economies with flexible exchange rates, where a contraction in government spending was associated with growth; but in these cases the hole in demand created by the government contraction was filled in with exports. Canada in the early 1990s was lucky because the United States was going through a rapid expansion, the recovery from the 1991 recession.


pages: 291 words: 80,068

Framers: Human Advantage in an Age of Technology and Turmoil by Kenneth Cukier, Viktor Mayer-Schönberger, Francis de Véricourt

Albert Einstein, Andrew Wiles, autonomous vehicles, Ben Bernanke: helicopter money, Berlin Wall, bitcoin, blockchain, circular economy, Claude Shannon: information theory, cognitive dissonance, coronavirus, correlation does not imply causation, COVID-19, credit crunch, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, defund the police, discovery of DNA, Donald Trump, double helix, Douglas Hofstadter, Elon Musk, en.wikipedia.org, fiat currency, framing effect, Francis Fukuyama: the end of history, Frank Gehry, game design, George Floyd, George Gilder, global pandemic, global village, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, informal economy, Isaac Newton, Jaron Lanier, Jeff Bezos, job-hopping, knowledge economy, Louis Pasteur, Mark Zuckerberg, Mercator projection, meta-analysis, microaggression, nudge unit, packet switching, pattern recognition, Peter Thiel, quantitative easing, Ray Kurzweil, Richard Florida, Schrödinger's Cat, scientific management, self-driving car, Silicon Valley, Steve Jobs, Steven Pinker, The Structural Transformation of the Public Sphere, Thomas Kuhn: the structure of scientific revolutions, Tim Cook: Apple, too big to fail, transaction costs, Tyler Cowen

He argued that causation cannot be justified rationally and that our inductive reasoning misleads us: just because the sun has always risen doesn’t mean it will rise tomorrow. In the same vein, traditional statisticians, driven by a well-meaning sense of caution, did much to prevent people from drawing causal inferences from data. They long insisted that we can only regard events as correlated or coincidental. “Correlation is not causation” was their battle cry—and the dogma they taught students on the very first day of class. On causality, they were mute. “They declared those questions off-limits,” explains Judea Pearl, the modern father of the “causal revolution” in computer science. The causal skepticism ascribed to Hume rests, at least in part, on a misunderstanding.

What was required were models that depended on counterfactuals—not the world as it is, but as it might be. “Temperature and carbon dioxide do not tell of cause and effect,” Fung, now a professor of climate science at Berkeley, explains in an interview. “Both ice cream sales and murder rates in New York City increase in the summer,” she quips, suggesting a correlation without causation. “Models represent our best approximation of the real system. They allow us to identify the processes that are responsible, as well as processes that are not responsible,” she explains. “Models are the only way to project what would happen if the carbon dioxide emission trajectory changes.”


pages: 416 words: 106,582

This Will Make You Smarter: 150 New Scientific Concepts to Improve Your Thinking by John Brockman

23andMe, Albert Einstein, Alfred Russel Wallace, banking crisis, Barry Marshall: ulcers, Benoit Mandelbrot, Berlin Wall, biofilm, Black Swan, butterfly effect, Cass Sunstein, cloud computing, congestion charging, correlation does not imply causation, Daniel Kahneman / Amos Tversky, dark matter, data acquisition, David Brooks, delayed gratification, Emanuel Derman, epigenetics, Exxon Valdez, Flash crash, Flynn Effect, Garrett Hardin, hive mind, impulse control, information retrieval, information security, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Jaron Lanier, Johannes Kepler, John von Neumann, Kevin Kelly, lifelogging, mandelbrot fractal, market design, Mars Rover, Marshall McLuhan, microbiome, Murray Gell-Mann, Nicholas Carr, open economy, Pierre-Simon Laplace, place-making, placebo effect, pre–internet, QWERTY keyboard, random walk, randomized controlled trial, rent control, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, Satyajit Das, Schrödinger's Cat, scientific management, security theater, selection bias, Silicon Valley, Stanford marshmallow experiment, stem cell, Steve Jobs, Steven Pinker, Stewart Brand, the scientific method, Thorstein Veblen, Turing complete, Turing machine, twin studies, Vilfredo Pareto, Walter Mischel, Whole Earth Catalog, WikiLeaks, zero-sum game

But the form of the causal connection is unspecified—a principle often stated as “correlation does not imply causation.” The reason for this is that the essence of causation as a concept rests on our tendency to have information about earlier events before we have information about later events. (The full implications of this concept for human consciousness, the second law of thermodynamics, and the nature of time are interesting, but sadly outside the scope of this essay.) If information about all events always came in the order in which the events occurred, then correlation would indeed imply causation. But in the real world, not only are we limited to observing events in the past but also we may discover information about those events out of order.

., 61 climate change, 51, 53, 99, 178, 201–2, 204, 268, 309, 315, 335, 386, 390 CO2 levels and, 202, 207, 217, 262 cultural differences in view of, 387–88 global economy and, 238–39 procrastination in dealing with, 209, 210 clinical trials, 26, 44, 56 cloning, 56, 165 coastlines, xxvi, 246 Cochran, Gregory, 360–62 coffee, 140, 152, 351 cognition, 172 perception and, 133–34 cognitive humility, 39–40 cognitive load, 116–17 cognitive toolkit, 333 Cohen, Daniel, 254 Cohen, Joel, 65 Cohen, Steven, 307–8 cold fusion, 243, 244 Coleman, Ornette, 254, 255 collective intelligence, 257–58 Colombia, 345 color, 150–51 color-blindness, 144 Coltrane, John, 254–55 communication, 250, 358, 372 depth in, 227 temperament and, 231 companionship, 328–29 comparative advantage, law of, 100 comparison, 201 competition, 98 complexity, 184–85, 226–27, 326, 327 emergent, 275 computation, 227, 372 computers, 74, 103–4, 146–47, 172 cloud and, 74 graphical desktops on, 135 memory in, 39–40 open standards and, 86–87 computer software, 80, 246 concept formation, 276 conduction, 297 confabulation, 349–52 confirmation bias, 40, 134 Conner, Alana, 367–70 Conrad, Klaus, 394 conscientiousness, 232 consciousness, 217 conservatism, 347, 351 consistency, 128 conspicuous consumption, 228, 308 constraint satisfaction, 167–69 consumers, keystone, 174–76 context, sensitivity to, 40 continental drift, 244–45 conversation, 268 Conway, John Horton, 275, 277 cooperation, 98–99 Copernicanism, 3 Copernican Principle, 11–12, 25 Copernicus, Nicolaus, 11, 294 correlation, and causation, 215–17, 219 creationism, 268–69 creativity, 152, 395 constraint satisfaction and, 167–69 failure and, 79, 225 negative capability and, 225 serendipity and, 101–2 Crick, Francis, 165, 244 criminal justice, 26, 274 Croak, James, 271–72 crude look at the whole (CLAW), 388 Crutzen, Paul, 208 CT scans, 259–60 cultural anthropologists, 361 cultural attractors, 180–83 culture, 154, 156, 395 change and, 373 globalization and, see globalization culture cycle, 367–70 cumulative error, 177–79 curating, 118–19 currency, central, 41 Cushman, Fiery, 349–52 cycles, 170–73 Dalrymple, David, 218–20 DALYs (disability-adjusted life years), 206 danger, proving, 281 Darwin, Charles, 2, 44, 89, 98, 109, 156, 165, 258, 294, 359 Das, Satyajit, 307–9 data, 303, 394 personal, 303–4, 305–6 security of, 76 signal detection theory and, 389–93 Dawkins, Richard, 17–18, 180, 183 daydreaming, 235–36 DDT, 125 De Bono, Edward, 240 dece(i)bo effect, 381–85 deception, 321–23 decision making, 52, 305, 393 constraint satisfaction and, 167–69 controlled experiments and, 25–27 risk and, 56–57, 68–71 skeptical empiricism and, 85 deduction, 113 defeasibility, 336–37 De Grey, Aubrey, 55–57 delaying gratification, 46 democracy, 157–58, 237 Democritus, 9 Demon-Haunted World, The (Sagan), 273 Dennett, Daniel C., 170–73, 212, 275 depth, 226–28 Derman, Emanuel, 115 Descent of Man, The (Darwin), 156 design: mind and, 250–53 recursive structures in, 246–49 determinism, 103 Devlin, Keith, 264–65 Diagnostic and Statistical Manual of Mental Disorders (DSM-5), 233–34 “Dial F for Frankenstein” (Clarke), 61 Diesel, Rudolf, 170 diseases, 93, 128, 174 causes of, 59, 303–4 distributed systems, 74–77 DNA, 89, 165, 223, 244, 260, 292, 303, 306 Huntington’s disease and, 59 sequencing of, 15 see also genes dopamine, 230 doughnuts, 68–69, 70 drug trade, 345 dualities, 296–98, 299–300 wave-particle, 28, 296–98 dual view of ourselves, 32 dynamics, 276 Eagleman, David, 143–45 Earth, 294, 360 climate change on, see climate change distance between sun and, 53–54 life on, 3–5, 10, 15 earthquakes, 387 ecology, 294–95 economics, 100, 186, 208, 339 economy(ies), 157, 158, 159 global, 163–64, 238–39 Pareto distributions in, 198, 199, 200 and thinking outside of time, 223 ecosystems, 312–14 Edge, xxv, xxvi, xxix–xxx education, 50, 274 applying to real-world situations, 40 as income determinant, 49 policies on, controlled experiments in, 26 scientific lifestyle and, 20–21 efficiency, 182 ego: ARISE and, 235–36 see also self 80/20 rule, 198, 199 Einstein, Albert, 28, 55, 169, 301, 335, 342 on entanglement, 330 general relativity theory of, 25, 64, 72, 234, 297 memory law of, 252 on simplicity, 326–27 Einstellung effect, 343–44 electrons, 296–97 Elliott, Andrew, 150 Eliot, T.


pages: 825 words: 228,141

MONEY Master the Game: 7 Simple Steps to Financial Freedom by Tony Robbins

3D printing, active measures, activist fund / activist shareholder / activist investor, addicted to oil, affirmative action, Affordable Care Act / Obamacare, Albert Einstein, asset allocation, backtesting, Bear Stearns, bitcoin, Black Monday: stock market crash in 1987, buy and hold, clean water, cloud computing, corporate governance, corporate raider, correlation does not imply causation, Credit Default Swap, currency risk, Dean Kamen, declining real wages, diversification, diversified portfolio, Donald Trump, estate planning, fear of failure, fiat currency, financial independence, fixed income, forensic accounting, high net worth, index fund, Internet of things, invention of the wheel, Jeff Bezos, John Bogle, junk bonds, Kenneth Rogoff, lake wobegon effect, Lao Tzu, London Interbank Offered Rate, Marc Benioff, market bubble, Michael Milken, money market fund, mortgage debt, new economy, obamacare, offshore financial centre, oil shock, optical character recognition, Own Your Own Home, passive investing, profit motive, Ralph Waldo Emerson, random walk, Ray Kurzweil, Richard Thaler, risk free rate, risk tolerance, riskless arbitrage, Robert Shiller, salary depends on his not understanding it, Salesforce, San Francisco homelessness, self-driving car, shareholder value, Silicon Valley, Skype, Snapchat, sovereign wealth fund, stem cell, Steve Jobs, subscription business, survivorship bias, tail risk, telerobotics, The future is already here, the rule of 72, thinkpad, transaction costs, Upton Sinclair, Vanguard fund, World Values Survey, X Prize, Yogi Berra, young professional, zero-sum game

RAINMAKER Ray was now on a roll and was systematically dissecting everything I had been taught or sold over the years! “Tony, there is another major problem with the balanced portfolio ‘theory.’ It’s based around a giant and, unfortunately, inaccurate assumption. It’s the difference between correlation and causation.” Correlation is a fancy investment word for when things move together. In primitive cultures, they would dance in an attempt to make it rain. Sometimes it actually worked! Or so they thought. They confused causation with correlation. In other words, they thought their jumping up and down caused the rain, but it was actually just coincidence.

., 530 Chantal (Rwandan orphan), 592–93 child slavery, 600 China, death by a thousand cuts in, 109 Churchill, Winston, 188, 244, 457, 588 clarity, as power, 611 Clason, George Samuel, 69 Clinton, Bill, 553 Cloonan, James, 87 clothing, breathable, 567 cloud computing, xxvii Club of Rome, 556 Coca-Cola, 460, 566 Coelho, Paulo, 225 cognitive illusions, 38–39 cognitive limitations, 41 cognitive understanding, 42 collectibles, 324 commodities, 324 community service, 342–43 complexity, 41, 206 compounding, 35–36, 49–52, 58, 256, 364 fees, 106–9, 479 financial breakthrough of, 192–93 rule of 72 in, 283 savings, 60, 62–65, 238, 280 and taxes, 235, 277–78, 279, 445–46 and time, 311, 312 Connally, John B., 372 connection, 77 consumer spending, 213, 562 contrast, 245 contribution, 77–78, 266–67, 585 control, illusion of, 422, 580 Coppola, Francis Ford, 6, 52–53, 60 corporate bonds, 318–19 correlation vs. causation, 384 cortisol, 197 Costa Rica, moving to, 291 cost calculator, 111 creativity, 193, 266–67 credit-default obligations (CDOs), 325 critical mass, 33, 58, 89, 90, 408 Cuban, Mark, 281 Cuddy, Amy, 197 Cunningham, Keith, 133–34 currencies, 324, 328, 353 currency risk, 328 currency swap, 469 Curry, Ann, 350 Dalai Lama 574–75 Dalio, Ray, 10, 21–24, 25, 30, 41, 84, 94, 106, 496 on active management, 165 and All Seasons/All Weather, 306, 370, 371–72, 374–92, 404, 448, 613 and asset allocation, 101, 163, 282–83, 296, 298, 299, 331, 379, 383–84, 388, 389, 412, 494 author’s interview with, 47, 448, 455, 496–97 and Bridgewater, 21, 99, 374–75, 397, 496–97 and futures contract, 374 How the Economic Machine Works, 380 and McDonald’s, 373–74 portfolio of, 23, 101, 372–73, 390–91, 437 and Pure Alpha, 375–76, 397 and Risk/Reward, 173 and volatility, 301, 321 Damon, Matt, 17 death by a thousand cuts, 109, 122 debt, 239–40, 275 decisions: financial, 295 investment, 295, 364 our lives determined by, 244, 246 defined benefit plans, 155 deflation, 329, 385, 386, 526 demographic inevitability, 285 demographic wave, 562 denial, 211 depreciation, 285–86 depression, 581–82, 594 Diamandis, Peter, 47, 551, 554–55, 564, 572 DiCaprio, Leonardo, 15 Dimensional Funds, 113, 143 Dimon, Jamie, 499 discipline, 199, 543 Disraeli, Benjamin, 248, 573 diversification, 325–26, 527–28 and asset allocation, 296, 297–300, 355, 363, 364, 378, 472–73, 482 and asset classes, 355, 363, 383, 473, 490–91 and index funds, 49, 357, 473, 483 and long-term investment, 474 and returns, 276, 282, 297 and risk/reward, 297, 300, 379, 383, 456, 472–73 and volatility, 104 Dodd, Chris, 122 Dodd-Frank Wall Street Reform and Consumer Protection Act (2009), 122–23, 135 dollar-cost averaging, 355–59, 363, 365–66, 613 dollar-weighted returns, 118–19, 121 Dow Jones Industrial Average, 101 Dream Bucket, 207, 339, 340–47, 363 asset allocation, 346, 347, 613 and community service, 342–43 filling, 343–44, 613 and gifts, 341–42 and lifestyle, 341 list your dreams, 345 state your goals, 345 strategic splurges in, 340 Dunn, Elizabeth, 589, 601 Duty Free Shopping (DFS), 72 Earhart, Amelia, 63 Earnhardt, Dale Sr., 321 earnings, and investment, 259–72 Ebates, 256 Edelen, Roger, 114 Edison, Thomas A., 19 education, 264, 265–66 teachers, 266–67 effort, 228 Egyptian Treasury bills, 319 Einhorn, David, 99 Einstein, Albert, 50, 83, 259, 292 Eisenson, Marc, 251 Elizabeth I, queen of England, 550 Elizabeth II, queen of England, 541 emergency/protection fund, 216–17, 302 emerging markets, 100, 358, 473, 527 Emerson, Ralph Waldo, 19, 59, 219 Eminem, 191 emotion, 191, 209, 210, 301, 355, 402, 582, 594 emotional mastery, 42 empowerment, 190 endowment model, 469 energy policy, 506, 509, 510–12, 556–57 Enriquez, Juan, 551, 563, 566 Enron, 133–34, 162–63 entrepreneurs: and automatic savings, 65, 69 cash-balance plan for, 155 and 401(k)s, 146–48, 152, 153, 181 environment, investment, 385–88 Epictetus, 37 equities, 322–23, 329–30, 473 Erdoes, Mary Callahan, 10, 99–100, 455, 498 on asset allocation, 296, 337, 504 author’s interview with, 100, 309, 337–38, 498–504 on leadership, 501 on long-term investment, 504 on rebalancing, 361 on structured notes, 309–10 Europe, economies in, 518–20 Evans, Richard, 114 exchange-traded funds (ETFs), 322–23 execution, 41, 65, 228, 388, 616 expectations, 334, 387 expense ratio, 108, 113 expenses, cutting, 253–56 Extrabux, 256 extracellular matrix (ECM), 568 Faber, Marc, 523–28, 523 Facebook, 270 failure to try, 271 Fama, Eugene, 98 Farrell, Charlie, 279 fate, 228–29, 343 fear: of being judged, 193 dealing with, 544–45 of failure, 183–84, 225, 301 physical effects of, 196 of the unknown, 185, 211 Federal Deposit Insurance Corporation (FDIC), 178–79, 302, 305 Federal Reserve, 354, 481, 524, 535 Fee Checker, 145, 148, 151–52, 181 Feeding America, 598, 599 Feeney, Chuck, 72–73, 595 fees, 87, 104, 236 of annuities, 168–69, 308, 434, 439 compounding, 106–9, 479 cost calculator, 111 in 401(k)s, 111, 114, 141, 142, 143–46, 148, 151–52, 181 on index funds, 112, 165, 278 of mutual funds, 105–15, 119, 121, 141, 180, 273, 278, 479 nondeductible, 112 in pensions, 86 reducing, 273–80 and risk/reward, 177, 180 in structured notes, 310 Feldstein, Martin, 385 fiduciary, 126–33 advice from, 126, 286, 319, 338, 362 brokers vs., 126–28, 137, 180 Butcher vs.


pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, algorithmic bias, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, Big Tech, bitcoin, Boeing 747, Boston Dynamics, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, data science, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Fellow of the Royal Society, Flash crash, future of work, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, Hans Rosling, ImageNet competition, income inequality, industrial robot, information retrieval, job automation, John von Neumann, Law of Accelerating Returns, life extension, Loebner Prize, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, natural language processing, new economy, opioid epidemic / opioid crisis, optical character recognition, pattern recognition, phenotype, Productivity paradox, radical life extension, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, Ted Kaczynski, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, zero-sum game, Zipcar

You can show a young child their first giraffe, and now they know what a giraffe looks like; you can show them a new gesture or dance move, or how you use a new tool, and right away they’ve got it; they may not be able to make that move themselves, or use that tool, but they start to grasp what’s going on. Or think about learning causality, for example. We learn in basic statistics classes that correlation and causation are not the same thing, and correlation doesn’t always imply causation. You can take a dataset, and you can measure that the two variables are correlated, but it doesn’t mean that one causes the other. It could be that A causes B, B causes A, or some third variable causes both. The fact that correlation doesn’t uniquely imply causation is often cited to show how difficult it is to take observational data and infer the underlying causal structure of the world, and yet humans do this.

That means that we lose transparency, we lose reconfigurability, and other nice features that we like. By the time that I published my book on Bayesian networks in 1988, though, I already felt like an apostate because I knew already that the next step would be to model causality, and my love was already on a different endeavor. MARTIN FORD: We always hear people saying that “correlation is not causation,” and so you can never get causation from the data. Bayesian networks do not offer a way to understand causation, right? JUDEA PEARL: No, Bayesian networks could work in either mode. It depends on what you think about when you construct it. MARTIN FORD: The Bayesian idea is that you update probabilities based on new evidence so that your estimate should get more accurate over time.

However, in practice, people noticed that if you structure the network in the causal direction, things are much easier. The question was why. Now we understand that we were craving for features of causality that we didn’t even know come from causality. These were: modularity, reconfigurability, transferability, and more. By the time I looked into causality, I had realized that the mantra “correlation does not imply causation” is much more profound than we thought. You need to have causal assumptions before you can get causal conclusions, which you cannot get from data alone. Worse yet, even if you are willing to make causal assumptions, you cannot express them. There was no language in science in which you can express a simple sentence like “mud does not cause rain,” or “the rooster does not cause the sun to rise.”


pages: 252 words: 72,473

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil

Affordable Care Act / Obamacare, Alan Greenspan, algorithmic bias, Bernie Madoff, big data - Walmart - Pop Tarts, call centre, carried interest, cloud computing, collateralized debt obligation, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, data science, disinformation, Emanuel Derman, financial engineering, Financial Modelers Manifesto, housing crisis, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, Ida Tarbell, illegal immigration, Internet of things, late fees, mass incarceration, medical bankruptcy, Moneyball by Michael Lewis explains big data, new economy, obamacare, Occupy movement, offshore financial centre, payday loans, peer-to-peer lending, Peter Thiel, Ponzi scheme, prediction markets, price discrimination, quantitative hedge fund, Ralph Nader, RAND corporation, recommendation engine, Rubik’s Cube, Salesforce, Sharpe ratio, statistical model, tech worker, Tim Cook: Apple, too big to fail, Unsafe at Any Speed, Upton Sinclair, Watson beat the top human players on Jeopardy!, working poor

This cruel practice, known as redlining, has been outlawed by various pieces of legislation, including the Fair Housing Act of 1968. Nearly a half century later, however, redlining is still with us, though in far more subtle forms. It’s coded into the latest generation of WMDs. Like Hoffman, the creators of these new models confuse correlation with causation. They punish the poor, and especially racial and ethnic minorities. And they back up their analysis with reams of statistics, which give them the studied air of evenhanded science. On this algorithmic voyage through life, we’ve clawed our way through education and we’ve landed a job (even if it is one that runs us on a chaotic schedule).


pages: 237 words: 65,794

Mining Social Media: Finding Stories in Internet Data by Lam Thuy Vo

barriers to entry, correlation does not imply causation, data science, Donald Trump, en.wikipedia.org, Filter Bubble, Firefox, Google Chrome, Internet Archive, natural language processing, social web, web application

By merging two sheets, we can easily compare values based on a common category. We should be cautious about drawing major conclusions about the relationship between data sets, however. Correlation is not the same as causation. This means even though two data sets may seem like they have a relationship—that is, the data sets seem correlated—that doesn’t mean that one data set caused the results in the other. Correlational and causational connections between data sets should be backed by other research from reports, experts, or field studies. But even the simplest comparisons between two or more data sets can be very illustrative.


pages: 836 words: 158,284

The 4-Hour Body: An Uncommon Guide to Rapid Fat-Loss, Incredible Sex, and Becoming Superhuman by Timothy Ferriss

23andMe, airport security, Albert Einstein, Black Swan, Buckminster Fuller, caloric restriction, caloric restriction, carbon footprint, cognitive dissonance, Columbine, correlation does not imply causation, Dean Kamen, game design, Gary Taubes, Gregor Mendel, index card, Kevin Kelly, knowledge economy, life extension, lifelogging, Mahatma Gandhi, microbiome, p-value, Parkinson's law, Paul Buchheit, placebo effect, Productivity paradox, publish or perish, radical life extension, Ralph Waldo Emerson, Ray Kurzweil, Recombinant DNA, Richard Feynman, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Silicon Valley startup, Skype, stem cell, Steve Jobs, survivorship bias, The future is already here, The Theory of the Leisure Class by Thorstein Veblen, Thorstein Veblen, Vilfredo Pareto, wage slave, William of Occam

Most “new studies” in the media are observational studies that can, at best, establish correlation (A happens while B happens), but not causality (A causes B to happen). If I pick my nose when the Super Bowl cuts to a commercial, did I cause that? This isn’t a haiku. It’s a summary: correlation doesn’t prove causation. Be skeptical when people tell you that A causes B. They’re wrong much more than 50% of the time. USE THE YO-YO: EMBRACE CYCLING Yo-yo dieting gets a bad rap. Instead of beating yourself up, going to the shrink, or eating an entire cheesecake because you ruined your diet with one cookie, allow me to deliver a message: it’s normal.

Observational studies can only show correlation: A and B both exist at the same time in one group. They cannot show cause and effect.4 In contrast, randomized and controlled experiments control variables and can therefore show cause and effect (causation): A causes B to happen. The satirical religion Pastafarianism purposely confuses correlation and causation: With a decrease in the number of pirates, there has been an increase in global warming over the same period. Therefore, global warming is caused by a lack of pirates. Even more compelling: Somalia has the highest number of Pirates AND the lowest Carbon emissions of any country.

Or you could get really serious and start to manipulate the statistics. For two pages only, this will now get quite nerdy. Here are the classic tricks to play in your statistical analysis to make sure your trial has a positive result. Ignore the protocol entirely Always assume that any correlation proves causation. Throw all your data into a spreadsheet programme and report—as significant—any relationship between anything and everything if it helps your case. If you measure enough, some things are bound to be positive just by sheer luck. Play with the baseline Sometimes, when you start a trial, quite by chance the treatment group is already doing better than the placebo group.


pages: 307 words: 96,543

Tightrope: Americans Reaching for Hope by Nicholas D. Kristof, Sheryl Wudunn

Affordable Care Act / Obamacare, air traffic controllers' union, basic income, Bernie Sanders, carried interest, correlation does not imply causation, creative destruction, David Brooks, Donald Trump, dumpster diving, Edward Glaeser, Elon Musk, epigenetics, full employment, Home mortgage interest deduction, housing crisis, impulse control, income inequality, Jeff Bezos, job automation, jobless men, knowledge economy, labor-force participation, low skilled workers, mandatory minimum, Martin Wolf, mass incarceration, Mikhail Gorbachev, offshore financial centre, opioid epidemic / opioid crisis, randomized controlled trial, rent control, Robert Shiller, Ronald Reagan, Savings and loan crisis, Shai Danziger, single-payer health, Steven Pinker, The Spirit Level, universal basic income, upwardly mobile, Vanguard fund, War on Poverty, working poor

“A father’s absence increases antisocial behavior, such as aggression, rule-breaking, delinquency and illegal drug use,” with the effects greater for boys than for girls, Sara McLanahan of Princeton University and Christopher Jencks of Harvard University concluded after assessing the evidence. Yet there’s a danger of drawing too sweeping a conclusion here, for it’s difficult to untangle correlation from causation, and in any case many single moms do brilliantly. In addition, most of the data is driven by low-income households, where a single parent means a constant financial struggle; more affluent single-parent households are much more likely to succeed. In any case, what matters isn’t a traditional family structure so much as stability.

“it makes it harder for me to get a job”: Resentment of Latino immigrants was rooted not only in lost jobs but also in frustration that the social status of white working-class men had plummeted, with demographic and cultural changes making them feel a little like, in Arlie Russell Hochschild’s phrase, “strangers in their own land.” 16. THE MARRIAGE OF TRUE MINDS success for black men was marriage: W. Bradford Wilcox, Wendy R. Wang and Ronald B. Mincy, “Black Men Making It in America,” American Enterprise Institute, 2018. Of course, that is correlation rather than causation, and some of the unmarried men had risk factors that also made them less marriageable. two-parent households have more social capital: Consider low-income black men growing up in two different neighborhoods in Los Angeles. Of young black men who grew up in the lowest-income families in Watts, 44 percent ended up incarcerated on a single day (the day of the 2010 census).


pages: 1,351 words: 385,579

The Better Angels of Our Nature: Why Violence Has Declined by Steven Pinker

1960s counterculture, affirmative action, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, availability heuristic, Berlin Wall, Boeing 747, Bonfire of the Vanities, British Empire, Broken windows theory, business cycle, California gold rush, Cass Sunstein, citation needed, clean water, cognitive dissonance, colonial rule, Columbine, computer age, Computing Machinery and Intelligence, conceptual framework, correlation coefficient, correlation does not imply causation, crack epidemic, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Brooks, delayed gratification, demographic transition, desegregation, Doomsday Clock, Douglas Hofstadter, Dr. Strangelove, Edward Glaeser, en.wikipedia.org, European colonialism, experimental subject, facts on the ground, failed state, first-past-the-post, Flynn Effect, food miles, Francis Fukuyama: the end of history, fudge factor, full employment, Garrett Hardin, George Santayana, ghettoisation, Gini coefficient, global village, Golden arches theory, Henri Poincaré, Herbert Marcuse, Herman Kahn, high-speed rail, Hobbesian trap, humanitarian revolution, impulse control, income inequality, informal economy, Intergovernmental Panel on Climate Change (IPCC), invention of the printing press, Isaac Newton, lake wobegon effect, libertarian paternalism, long peace, longitudinal study, loss aversion, Marshall McLuhan, mass incarceration, McMansion, means of production, mental accounting, meta-analysis, Mikhail Gorbachev, moral panic, mutually assured destruction, Nelson Mandela, open economy, Peace of Westphalia, Peter Singer: altruism, QWERTY keyboard, race to the bottom, Ralph Waldo Emerson, random walk, Republic of Letters, Richard Thaler, Ronald Reagan, Rosa Parks, Saturday Night Live, security theater, Skype, Slavoj Žižek, South China Sea, Stanford marshmallow experiment, Stanford prison experiment, statistical model, stem cell, Steven Levy, Steven Pinker, sunk-cost fallacy, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, theory of mind, Tragedy of the Commons, transatlantic slave trade, Turing machine, twin studies, ultimatum game, uranium enrichment, Vilfredo Pareto, Walter Mischel, WikiLeaks, women in the workforce, zero-sum game

(An amusing video reel of fulminating fogies can be seen in Cleveland’s Rock and Roll Hall of Fame and Museum.) Do we now have to—gulp—admit they were right? Can we connect the values of 1960s popular culture to the actual rise in violent crimes that accompanied them? Not directly, of course. Correlation is not causation, and a third factor, the pushback against the values of the Civilizing Process, presumably caused both the changes in popular culture and the increase in violent behavior. Also, the overwhelming majority of baby boomers committed no violence whatsoever. Still, attitudes and popular culture surely reinforce each other, and at the margins, where susceptible individuals and subcultures can be buffeted one way or another, there are plausible causal arrows from the decivilizing mindset to the facilitation of actual violence.

Also, cultures that are classified as more individualistic, where people feel they are individuals with the right to pursue their own goals, have relatively less domestic violence against women than the cultures classified as collectivist, where people feel they are part of a community whose interests take precedence over their own.94 These correlations don’t prove causation, but they are consistent with the suggestion that the decline of violence against women in the West has been pushed along by a humanist mindset that elevates the rights of individual people over the traditions of the community, and that increasingly embraces the vantage point of women.

On the contrary, “they must be permitted . . . the foolish and childish actions suitable to their years.”168 The idea that the way children are treated determines the kinds of adults they grow into is conventional wisdom today, but it was news at the time. Several of Locke’s contemporaries and successors turned to metaphor to remind people about the formative years of life. John Milton wrote, “The childhood shows the man as morning shows the day.” Alexander Pope elevated the correlation to causation: “Just as the twig is bent, the tree’s inclined.” And William Wordsworth inverted the metaphor of childhood itself: “The child is father of the man.” The new understanding required people to rethink the moral and practical implications of the treatment of children. Beating a child was no longer an exorcism of malign forces possessing a child, or even a technique of behavior modification designed to reduce the frequency of bratty behavior in the present.


pages: 105 words: 18,832

The Collapse of Western Civilization: A View From the Future by Naomi Oreskes, Erik M. Conway

anti-communist, correlation does not imply causation, creative destruction, en.wikipedia.org, energy transition, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Kim Stanley Robinson, laissez-faire capitalism, market fundamentalism, mass immigration, means of production, military-industrial complex, oil shale / tar sands, Pierre-Simon Laplace, precautionary principle, road to serfdom, Ronald Reagan, stochastic process, the built environment, the market place

fisherian statistics A form of mathematical analysis developed in the early twentieth century and designed to help distinguish between causal and accidental relation-ships between phenomena. Its originator, R. A. Fisher, was one of the founders of the science of population genetics, and also an advocate of racially-based eugenics programs. Fisher also rejected the evidence that tobacco use caused cancer, and his argument that “correlation is not causation” was later used as a mantra by neoliberals rejecting the scientific evidence of various forms of adverse environmental and health effects from industrial products (see statistical significance). fugitive emissions Leakage from wellheads, pipelines, refineries, etc. Considered “fugitive” because the releases were supposedly unintentional, at least some of them (e.g., methane venting at oil wells) were in fact entirely deliberate.


pages: 554 words: 149,489

The Content Trap: A Strategist's Guide to Digital Change by Bharat Anand

Airbnb, Alan Greenspan, An Inconvenient Truth, Benjamin Mako Hill, Bernie Sanders, Clayton Christensen, cloud computing, commoditize, correlation does not imply causation, creative destruction, crowdsourcing, death of newspapers, disruptive innovation, Donald Trump, Eyjafjallajökull, fulfillment center, gamification, Google Glasses, Google X / Alphabet X, information asymmetry, Internet of things, inventory management, Jean Tirole, Jeff Bezos, John Markoff, Just-in-time delivery, Khan Academy, Kickstarter, late fees, managed futures, Mark Zuckerberg, market design, Minecraft, multi-sided market, Network effects, post-work, price discrimination, publish or perish, QR code, recommendation engine, ride hailing / ride sharing, Salesforce, selection bias, self-driving car, shareholder value, Shenzhen special economic zone , Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, social graph, social web, special economic zone, Stephen Hawking, Steve Jobs, Steven Levy, Thomas L Friedman, transaction costs, two-sided market, ubercab, WikiLeaks, winner-take-all economy, zero-sum game

Figure 14: Impact of Format Changes on Music Sales, 1973–2013 (Peak unit sales normalized to 100 for all formats) Diagnosing the music industry problem is not simply a question of seeing that CD declines are coincident with trends in file sharing. It requires separating cause from effect. The problem with the diagnosis stems from an age-old problem in statistical inference: separating correlation from causation. We see it everywhere. Does TV viewing increase obesity, or are obese individuals more inclined to watch TV? Are Asians innately better at math, or do they work harder at it? Simple correlations would lead you to infer that there’s some causal relation between two variables, when in fact there might be none.

Was eBay stupid in that it was using the wrong keywords? Absolutely not—by then eBay had learned a lot about keyword bidding from prediction models developed by a very sophisticated group of Ph.D. computer scientists. But the models fell under the category of machine learning, where all you care about is correlation, not causation. Was eBay wasting a lot of money? Yes—just like any company that’s not aware. And that’s practically all companies using the industry’s best practices, which are flawed because of the endogeneity problem. eBay hadn’t set out to understand how the endogeneity problem affected the returns on paid search.


pages: 586 words: 159,901

Wall Street: How It Works And for Whom by Doug Henwood

accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, affirmative action, Alan Greenspan, Andrei Shleifer, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, bond market vigilante , borderless world, Bretton Woods, British Empire, business cycle, buy the rumour, sell the news, capital asset pricing model, capital controls, central bank independence, computerized trading, corporate governance, corporate raider, correlation coefficient, correlation does not imply causation, credit crunch, currency manipulation / currency intervention, currency risk, David Ricardo: comparative advantage, debt deflation, declining real wages, deindustrialization, dematerialisation, disinformation, diversification, diversified portfolio, Donald Trump, equity premium, Eugene Fama: efficient market hypothesis, experimental subject, facts on the ground, financial deregulation, financial engineering, financial innovation, Financial Instability Hypothesis, floating exchange rates, full employment, George Akerlof, George Gilder, hiring and firing, Hyman Minsky, implied volatility, index arbitrage, index fund, information asymmetry, interest rate swap, Internet Archive, invisible hand, Irwin Jacobs, Isaac Newton, joint-stock company, Joseph Schumpeter, junk bonds, kremlinology, labor-force participation, late capitalism, law of one price, liberal capitalism, liquidationism / Banker’s doctrine / the Treasury view, London Interbank Offered Rate, Louis Bachelier, market bubble, Mexican peso crisis / tequila crisis, Michael Milken, microcredit, minimum wage unemployment, money market fund, moral hazard, mortgage debt, mortgage tax deduction, Myron Scholes, oil shock, Paul Samuelson, payday loans, pension reform, plutocrats, Post-Keynesian economics, price mechanism, price stability, prisoner's dilemma, profit maximization, publication bias, Ralph Nader, random walk, reserve currency, Richard Thaler, risk tolerance, Robert Gordon, Robert Shiller, Savings and loan crisis, selection bias, shareholder value, short selling, Slavoj Žižek, South Sea Bubble, stock buybacks, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Market for Lemons, The Nature of the Firm, The Predators' Ball, The Wealth of Nations by Adam Smith, transaction costs, transcontinental railway, women in the workforce, yield curve, zero-coupon bond

Recently, however, that relationship seems to have broken down. Why this should be isn't clear; it could be that both the stock market and consumer spending were independently responding to lower interest rates, and that the conclusion that stocks were "causing" the spending changes are a classic example of confusing correlation with causation. Or it may be that the increasing institutionalization of the market has reduced the effect of stock prices on personal spending. Or it may have been that household balance sheets were in such terrible shape in the early 1990s that a bull market was of little help (Steindel 1992). But whatever the reason, this household application of q theory isn't quite as impressive as it once was.

., 249-251, 297 Colby, William, 104 Colgate-Palmolive, 113 College Retirement Equities Fund (CREF), 289 Collier, Sophia, 310 Columbia Savings, 88 commercial banks, 81-84 commodity prices, futures markets and, 33; see also futures markets common stock, 12 Community Capital Bank, 311 community development banks, 311-314 community development organizations, co-optation of, 315 community land trusts, 314-315 compensatory borrowing, 65 competition managerialist view of, 260 return of, 1970s, 260 Comstock, Lyndon, 311 Conant, Charles, 94-95 Conference Board, 136, 291 consciousness credit and, 236-237 rentier, profit with passage of time, 238 consensus, 133 consumer credit, 64-66, 77 in a Marxian light, 234 in 1930s depression, 156-157 rare in Keynes's day, 242 see also households Consumer Expenditure Survey, 70 consumption, 189 contracts, 249; see afao transactions-cost economics control. 5ee corporations, governance cooperatives, 321 managers hired by workers, 239 weaknesses of ownership structure, 88 corporate control, market for, 277-282 Manne on, 278 corporations debt distribution of, 1980s, 159 and early 1990s slump, 158-161 development, and stock market, 14 emergence, and Federal Reserve, 92-96 emergence of complex ownership, 188 evolution, 253 form as presaging worker control (Marx), 239-240 importance of railroads in emergence, 188 localist critique of, 241 managers' concern for stock price, 171 multinational evolution, and financial markets, 112-113 investment clusters, 111-112 nonfinancial, 72-76 finances (table), 75 financial interests, 262 refinancing in early 1990s, l6l role in economic analysis, 248 shareholders conribute nothing or less, 238 soulful, 258, 263; see afeo social investing stock markets' role in constitution of, 254 transforming, 320-321 virtues of size, 282 corporations, governance, 246-294 Baran and Sweezy on, 258 Berle and Means on, 252-258 abuse of owners by managers, 254 interest-group model, 257-258 Berle on collective capitalism, 253-254 boaids of directors, 27-29, 246, 257, 259, 263, 272 financial representatives on, 265 keiretsu, 275 of a "Morganized" firm, 264 rentier agenda, 290 structure, 299 competition's obsolescence/return, 260 debt and equity, differences, 247 EM theory and Jensenism as unified field theory, 276 financial control 359 WALL STREET meaning, 264 theories of, rebirth in 1970s, 260-263 financial interests asserted in crisis, 265 financial upsurge since 1980s, 263-265 Fitch/Oppenheimer controversy, 261-262 Galbraith on, 258-260 Golden Age managerialism, 258-260 Herman on, 260 influence vs. ownership, 264—265 international comparisons, 248 Jensenism. 5eeJensen, Michael market for corporate control, 277-282 narrowness of debate, 246 Rathenau on, 256 shareholder activism of 1990s, 288-291 Smith on, 255-256 Spencer on, 256-257 stockholder-bondholder conflicts, 248 theoretical taxonomy, 251-252 transactions cost economics, 248-251 transformation, 320-321 useless shareholders, 291-294 correlation coefficient, 116 correlation vs. causation, 145 cost of capital, 184, 298 Council of Institutional Investors, 290 Cowles, Alfred, 164 crack spread, 31 Cramer, James, 103 crank, 243 credit/credit markets assets, holders of, 59-61 as barrier to growth, 237 as boundary-smasher (Marx), 235 centrality of, 118-121 and consciousness, 236-237 European vs.


pages: 863 words: 159,091

A Manual for Writers of Research Papers, Theses, and Dissertations, Eighth Edition: Chicago Style for Students and Researchers by Kate L. Turabian

Bretton Woods, conceptual framework, correlation does not imply causation, illegal immigration, information security, Menlo Park, meta-analysis, Steven Pinker, Telecommunications Act of 1996, two and twenty, W. E. B. Du Bois, yellow journalism, Zeno's paradox

Such distinctions help you avoid mistakes like this: Original by Jones: We cannot conclude that one event causes another because the second follows the first. Nor can statistical correlation prove causation. But no one who has studied the data doubts that smoking is a causal factor in lung cancer. Misleading report: Jones claims “we cannot conclude that one event causes another because the second follows the first. Nor can statistical correlation prove causation.” Therefore, statistical evidence is not a reliable indicator that smoking causes lung cancer. 4.3.4 Categorize Your Notes for Sorting Finally, a conceptually demanding task: as you take notes, categorize the content of each one under two or more different keywords (see the upper right corner of the note card in fig. 4.1).


pages: 433 words: 129,636

Dreamland: The True Tale of America's Opiate Epidemic by Sam Quinones

1960s counterculture, Affordable Care Act / Obamacare, Albert Einstein, British Empire, call centre, centralized clearinghouse, correlation does not imply causation, crack epidemic, deindustrialization, do what you love, feminist movement, illegal immigration, mass immigration, Maui Hawaii, McMansion, obamacare, pill mill, zero-sum game

Never in thirty years of statistical mechanics had Orman Hall heard of a correlation that close to 1.0, which was almost as if the charts were saying that dispensing prescription painkillers was the same thing as people dying. Gay couldn’t believe it either. He ran the DOH numbers again. Each time, 0.979 appeared on his computer screen. Every statistician knows correlation does not mean causation. But to Gay the correlations did mean that Ohio could all but predict one overdose death for roughly every two months’ worth of prescription opiates dispensed. A Pro Wrestler’s Legacy Seattle, Washington In 2007, Alex Cahana opened the door to what had been John Bonica’s Center for Pain Relief at the University of Washington and found a cobwebbed relic.

One study estimated the country would need fifty-two thousand more primary care docs by 2025. A commentary by four doctors and researchers in the American Journal of Public Health in September 2014 insisted that “It is difficult to believe that the parallel rise in prescriptions and associated harms is mere correlation without causation. [Also] it is difficult to believe that the problem is solely attributable to patients with already existing substance use disorders.” They went on, “Appropriate medical use of prescription opioidscan, in some unknown proportion of cases, initiate a progression toward misuse and ultimately addiction . . .


pages: 502 words: 107,657

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

Alan Greenspan, Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, butter production in bangladesh, call centre, Charles Lindbergh, commoditize, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, data science, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, information security, job satisfaction, Johann Wolfgang von Goethe, lifelogging, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mass immigration, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, Shai Danziger, software as a service, SpaceShipOne, speech recognition, statistical model, Steven Levy, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra, zero-sum game

Public health offices in the UK Band members benefit from peer support and solo artists exhibit even riskier behaviour. Correlation Does Not Imply Causation Satisfaction came in the chain reaction. —From the song “Disco Inferno,” by The Trammps The preceding tables, packed with fun-filled facts, do not explain a single thing. Take note, the third column is headed “Suggested Explanation.” The left column’s discoveries are real, validated by data, but the reasons behind them are unknown. Every explanation put forth, each entry in the rightmost column, is pure conjecture with absolutely no hard facts to back it up. The dilemma is, as it is often said, correlation does not imply causation.5 The discovery of a predictive relationship between A and B does not mean one causes the other, not even indirectly.

Insights: The Factors behind Quitting Delivering Dynamite Don’t Quit While You’re Ahead Predicting Crime to Stop It Before It Happens The Data of Crime and the Crime of Data Machine Risk without Measure The Cyclicity of Prejudice Good Prediction, Bad Prediction The Source of Power Chapter 3: The Data Effect (data) The Data of Feelings and the Feelings of Data Predicting the Mood of Blog Posts The Anxiety Index Visualizing a Moody World Put Your Money Where Your Mouth Is Inspiration and Perspiration Sifting Through the Data Dump The Instrumentation of Everything We Do Batten Down the Hatches: T.M.I. The Big Bad Wolf The End of the Rainbow Prediction Juice Far Out, Bizarre, and Surprising Insights Correlation Does Not Imply Causation The Cause and Effect of Emotions A Picture Is Worth a Thousand Diamonds Validating Feelings and Feeling Validated Serendipity and Innovation Investment Advice from the Blogosphere Money Makes the World Go ‘Round Putting It All Together Chapter 4: The Machine That Learns (modeling) Boy Meets Bank Bank Faces Risk Prediction Battles Risk Risky Business The Learning Machine Building the Learning Machine Learning from Bad Experiences How Machine Learning Works Decision Trees Grow on You Computer, Program Thyself Learn Baby Learn Bigger Is Better Overlearning: Assuming Too Much The Conundrum of Induction The Art and Science of Machine Learning Feeling Validated: Test Data Carving Out a Work of Art Putting Decision Trees to Work for Chase Money Grows on Trees The Recession—Why Microscopes Can’t Detect Asteroid Collisions After Math Chapter 5: The Ensemble Effect (ensembles) Casual Rocket Scientists Dark Horses Mindsourced: Wealth in Diversity Crowdsourcing Gone Wild Your Adversary Is Your Amigo United Nations Meta-Learning A Big Fish at the Big Finish Collective Intelligence The Wisdom of Crowds . . . of Models A Bag of Models Ensemble Models in Action The Generalization Paradox: More Is Less The Sky’s the Limit Chapter 6: Watson and the Jeopardy!


pages: 523 words: 61,179

Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty, H. James Wilson

3D printing, AI winter, algorithmic management, algorithmic trading, Amazon Mechanical Turk, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, circular economy, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, data science, digital twin, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, fail fast, friendly AI, fulfillment center, future of work, Hans Moravec, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, Marc Benioff, natural language processing, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, robotic process automation, Rodney Brooks, Salesforce, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, software as a service, speech recognition, tacit knowledge, telepresence, telepresence robot, text mining, the scientific method, uber lyft, warehouse automation, warehouse robotics

“It was the first time that I know of that machines discovered new medical knowledge,” says Hill. “Straight from the data. There was no human involved in this discovery.”7 GNS Healthcare is showing that it’s possible, when AI is injected into the hypothesis phase of the scientific method, to find previously hidden correlations and causations. Moreover, use of the technology can result in dramatic cost savings. In one recent success, GNS was able to reverse-engineer—without using a hypothesis or preexisting assumptions—PCSK9, a class of drug that reduces bad cholesterol in the bloodstream. It took seventy years to discover PCSK9 and tens of billions of dollars over decades.


Logically Fallacious: The Ultimate Collection of Over 300 Logical Fallacies (Academic Edition) by Bo Bennett

Black Swan, butterfly effect, clean water, cognitive bias, correlation does not imply causation, Donald Trump, equal pay for equal work, Richard Feynman, side project, statistical model, sunk-cost fallacy, the scientific method

Tip: Pick up an introductory book to quantum physics, it is not only a fascinating subject, but you will be well prepared to ask the right questions and expose this fallacy when used. Questionable Cause cum hoc ergo propter hoc (also known as: ignoring a common cause, neglecting a common cause, confusing correlation and causation, confusing cause and effect, false cause, third cause, juxtaposition [form of], reversing causality/wrong direction [form of]) Description: Concluding that one thing caused another, simply because they are regularly associated. Logical Form: A is regularly associated with B, therefore, A causes B.


pages: 337 words: 103,522

The Creativity Code: How AI Is Learning to Write, Paint and Think by Marcus Du Sautoy

3D printing, Ada Lovelace, Albert Einstein, algorithmic bias, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Charles Babbage, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data is the new oil, data science, Donald Trump, double helix, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, mandelbrot fractal, Minecraft, music of the spheres, Narrative Science, natural language processing, Netflix Prize, PageRank, pattern recognition, Paul Erdős, Peter Thiel, random walk, Ray Kurzweil, recommendation engine, Rubik’s Cube, Second Machine Age, Silicon Valley, speech recognition, Turing test, Watson beat the top human players on Jeopardy!, wikimedia commons

The algorithm was programmed to calculate the effect on the score of moving left or right given the current state of the screen. The impact of a move could be several seconds down the line, so you have to calculate the delayed impact. This is quite tricky because it isn’t always clear what causes a certain effect. This is one of the shortcomings of machine learning: it sometimes picks up correlation and believes it to be causation. Animals suffer from the same problem. This is rather beautifully illustrated by an experiment that revealed pigeons to be superstitious. A number of pigeons were filmed in their cages and, at certain moments during the day, a food dispenser was moved into the cage. The door to the dispenser was on a delay so the pigeons, although excited by the arrival of the food dispenser, would have to wait to get the food.

E. 139 Beveridge, Andrew 56 Beyond the Fence (musical) 290–1 Białystok University 236 biases and blind spots, algorithmic 91–5 Birtwistle, Harrison 193 Blake, William 279 Blombos Cave, South Africa 103 Bloom (app) 229 BOB (artificial life form) 146–8 Boden, Margaret 9, 10, 11, 16, 39, 209, 222 Boeing 114 Bonaparte, Napoleon 158 bone carvings 104–5 booksellers 62–5 bordeebook 62–5 Borges, Jorge Luis: ‘The Library of Babel’ 241–4, 253, 304 Botnik 284–6 Boulanger, Nadia 186, 189, 205, 209 Boulez, Pierre 11, 223 brachistochrone 244 Braff, Zach 284 brain: biases and blind spots 91–2; consciousness and 274, 304–5; fractals and 124–5; mathematics and 155, 156, 160–1, 171, 174, 177, 178; musical composition and 187, 189, 193, 203, 205, 231; neural networks and 68–71, 68, 70; pattern recognition and 6, 20–1, 99–101, 155; stroke and 133–4; visual recognition and 76, 79, 143–4 Breakout (game) 26–8, 91, 92, 210 Brew, Jamie 284 Brin, Sergey 48–9, 51–2, 57 Bronowski, Jacob 104 Brown, Glenn 141 Bruner, Jerome 303 Buolamwini, Joy 94 Cage, John 106, 206 Calculus of Constructions (CoC) 173–4 see also Coq Cambridge Analytica 296 Cambridge University 18–19, 23–4, 43, 72, 81, 150, 225, 240, 278, 290 Carpenter, Loren 114, 115 Carré, Benoit 224 cars, driverless 6, 29–30, 79, 91 Cartesian geometry 110–11 Catmull, Ed 115 cave art, ancient 103–4, 105, 156, 230 Cawelti, John: Adventure, Mystery and Romance 252–3 Chang, Alex 23 chaos theory 124 Cheng, Ian 146–8 chess 16, 18–20, 21, 22–3, 29, 32–3, 34, 97, 151, 153, 162, 163, 246, 260–1, 304 child pornography 77 Chilvers, Peter 229 Chinese Room experiment 164, 273–5 Chomsky, Noam 260 Chopin, Frédéric 13, 197, 200, 202, 204, 206–7, 304 Christie’s 141 classemes 138 Classical era of music 10, 12–13, 190, 199, 207 Classification of Finite Simple Groups 18, 172, 175, 177, 244 Coelho, Paulo 302 Cohen, Harold 116–17, 118, 121 Coleridge, Samuel Taylor: ‘Kubla Khan’ 14 Colton, Simon 119, 120, 121–2, 291, 292, 293 Coltrane, John 223 Commodore Amiga 23 Congo (chimp) 107 consciousness 107, 231, 232, 270, 274, 283, 300, 302–6 Continuator, The 218–21, 286 Conway, John 18–19 Cope, David 195–203, 207, 208, 210, 304 copyright ownership 108–9 Coq 173–6, 177, 184 Coquand, Thierry 173 correlation as causation, mistaking 92–4 Corresponding Society of Musical Sciences 193, 208 Coulom, Rémi 31 Crazy Stone 31 Creative Adversarial Networks 140–1 creativity: algorithmic and rule-based, as 5; animals and 107–9; art, definition of and 103–7; audiences and 303; coder to code, shifting from 7, 102–3, 116–22, 132–42, 219–20; combinational 10–11, 16, 181, 222, 299; commercial incentive and 131–2; competition and 132–42; consciousness and 301–2, 303–5; death and 304; definition of 3–5, 9–13, 301–2; drugs and 181–2; exploratory 9–10, 40, 181, 219, 299; failure as component part of 17; feedback from others and 132; flow and 221–4, 222; Go and see Go; human lives as act of 303–4; Lovelace Test and see Lovelace Test; mathematics and 3, 150–1, 153, 161, 167–8, 170, 181–2, 185, 245–8, 253, 279–80; mechanical nature of 298; music and see music; new/novelty and 3, 4, 7–8, 12, 13, 16, 17, 40–3, 102–3, 109, 138–41, 140, 167–8, 238–9, 291–3, 299, 301; origins of our obsession with 301; political role of 303; randomness and 117–18; romanticising 14–15; self-reflection and 300; storytelling and see storytelling; surprise and 4, 8, 40, 65, 66, 102–3, 148, 168, 202, 241, 248–9; teaching 13–17; three types of 9–13; transformational 11–13, 17, 39, 41, 181, 209, 299; value and 4, 8, 12, 16, 17, 40–1, 102–3, 167–8, 238–9, 301, 304 Csikszentmihalyi, Mihaly 221 Cubism 11, 138, 139 Cybernetic Poet 280–2 Cybernetic Serendipity (ICA exhibition, 1968) 118–19 Dahl, Roald: Tales of the Unexpected 276–7; ‘The Great Automatic Grammatizator’ 276–7, 297 dating/matching 57–61, 58, 59, 60 da Vinci, Leonardo 106, 118, 128; Treatise on Painting 117 Davis, Miles: Kind of Blue 214 Debussy, Claude 1 DeepBach 210–12, 232 DeepBlue 29, 214, 260–1 DeepMind 25–43, 65, 95, 97, 98, 131, 132, 151, 210, 233–9, 241, 266 Deep Watch 224 Delft University of Technology 127 democracy 165–6 Dennett, Daniel 147 Descartes, René 12, 110–11 Disney 289–90 Duchamp, Marcel 106 du Sautoy, Marcus: attempts to fake a Jackson Pollock 123–5; composes music 186–8; The Music of the Primes 285–6; uses AI to write section of this book 297 Dylan, Bob 223 EEG 125 Egyptians, Ancient 157, 165 eigenvectors of matrices 53 Eisen, Michael 62, 64 Elgammal, Ahmed 132–3, 134, 135, 139, 140, 141 Eliot, George 302 Eliot, T.


pages: 293 words: 81,183

Doing Good Better: How Effective Altruism Can Help You Make a Difference by William MacAskill

barriers to entry, basic income, Black Swan, Branko Milanovic, Cal Newport, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, corporate social responsibility, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Brooks, Edward Jenner, effective altruism, en.wikipedia.org, end world poverty, experimental subject, follow your passion, food miles, immigration reform, income inequality, index fund, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, job automation, job satisfaction, Lean Startup, M-Pesa, mass immigration, meta-analysis, microcredit, Nate Silver, Peter Singer: altruism, purchasing power parity, quantitative trading / quantitative finance, randomized controlled trial, self-driving car, Skype, Stanislav Petrov, Steve Jobs, Steve Wozniak, Steven Pinker, The Future of Employment, The Wealth of Nations by Adam Smith, Tyler Cowen, universal basic income, women in the workforce

In 1950, life expectancy in sub-Saharan Africa was just 36.7 years. Now it’s 56 years, a gain of almost 50 percent. The picture that Dambisa Moyo paints is inaccurate. In reality, a tiny amount of aid has been spent, and there have been dramatic increases in the welfare of the world’s poorest people. Of course, correlation is not causation. Merely showing that the people’s welfare has improved at the same time the West has been offering aid does not prove that aid caused the improvement. It could be that aid is entirely incidental, or even harmful, holding back even greater progress that would have happened anyway or otherwise.

Robustness of evidence is very important for the simple reason that many programs don’t work, and it’s hard to distinguish the programs that don’t work from the programs that do. If we’d assessed Scared Straight by looking just at before-and-after delinquency rates for individuals who went through the program, we would have concluded it was a great program. Only after looking at randomized controlled trials could we tell that correlation did not indicate causation in this case and that Scared Straight programs were actually doing more harm than good. One of the most damning examples of low-quality evidence concerns microcredit (that is, lending small amounts of money to the very poor, a form of microfinance most famously associated with Muhammad Yunus and the Grameen Bank).


pages: 332 words: 104,587

Half the Sky: Turning Oppression Into Opportunity for Women Worldwide by Nicholas D. Kristof, Sheryl Wudunn

agricultural Revolution, correlation does not imply causation, demographic dividend, feminist movement, Flynn Effect, illegal immigration, Mahatma Gandhi, microcredit, paper trading, rolodex, Ronald Reagan, Rosa Parks, school choice, Shenzhen special economic zone , special economic zone, transatlantic slave trade, women in the workforce

“The evidence, in most cases, suffers from obvious biases: educated girls come from richer families and marry richer, more educated, more progressive husbands,” notes Esther Duflo of MIT, one of the most careful scholars of gender and development. “As such, it is, in general, difficult to account for all these factors, and few of the studies have tried to do so.” Correlation, in short, is not causation.* Advocates also undermine the trustworthiness of their cause by cherry-picking evidence. While we argue that schooling girls does stimulate economic growth and foster stability, for example, it is also true that one of the most educated parts of rural India is the state of Kerala, which has stagnated economically.

Speaking of role models and the power of education, Camfed Zimbabwe has a new and dynamic executive director. She’s a young woman who knows something about overcoming long odds and the impact a few dollars in tuition assistance can make in a girl’s life. It’s Angeline. * Larry Summers offers an example to emphasize the distinction between correlation and causation. He notes that there is an almost perfect correlation between literacy and ownership of dictionaries. But handing out more dictionaries will not raise literacy. CHAPTER ELEVEN Microcredit: The Financial Revolution It is impossible to realize our goals while discriminating against half the human race.


pages: 220 words: 66,518

The Biology of Belief: Unleashing the Power of Consciousness, Matter & Miracles by Bruce H. Lipton

Albert Einstein, Benoit Mandelbrot, Boeing 747, correlation does not imply causation, data science, discovery of DNA, double helix, Drosophila, epigenetics, Isaac Newton, Mahatma Gandhi, mandelbrot fractal, Mars Rover, On the Revolutions of the Heavenly Spheres, phenotype, placebo effect, randomized controlled trial, selective serotonin reuptake inhibitor (SSRI), stem cell

Read those articles closely and you’ll see that behind the breathless headline is a more sober truth. Scientists have linked lots of genes to lots of different diseases and traits, but scientists have rarely found that one gene causes a trait or a disease. The confusion occurs when the media repeatedly distort the meaning of two words: correlation and causation. It’s one thing to be linked to a disease; it’s quite another to cause a disease, which implies a directing, controlling action. If I show you my keys and say that a particular key “controls” my car, you at first might think that makes sense because you know you need that key to turn on the ignition.


pages: 295 words: 66,824

A Mathematician Plays the Stock Market by John Allen Paulos

Alan Greenspan, Benoit Mandelbrot, Black-Scholes formula, Brownian motion, business climate, business cycle, butter production in bangladesh, butterfly effect, capital asset pricing model, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, diversified portfolio, dogs of the Dow, Donald Trump, double entry bookkeeping, Elliott wave, endowment effect, Erdős number, Eugene Fama: efficient market hypothesis, four colour theorem, George Gilder, global village, greed is good, index fund, intangible asset, invisible hand, Isaac Newton, John Bogle, John Nash: game theory, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, mental accounting, Myron Scholes, Nash equilibrium, Network effects, passive investing, Paul Erdős, Paul Samuelson, Ponzi scheme, price anchoring, Ralph Nelson Elliott, random walk, Richard Thaler, risk free rate, Robert Shiller, short selling, six sigma, Stephen Hawking, stocks for the long run, survivorship bias, transaction costs, two and twenty, ultimatum game, Vanguard fund, Yogi Berra

To find the volatility of a portfolio in general, we need what is called the “covariance” (closely related to the correlation coefficient) between any pair of stocks X and Y in the portfolio. The covariance between two stocks is roughly the degree to which they vary together—the degree, that is, to which a change in one is proportional to a change in the other. Note that unlike many other contexts in which the distinction between covariance (or, more familiarly, correlation) and causation is underlined, the market generally doesn’t care much about it. If an increase in the price of ice cream stocks is correlated to an increase in the price of lawn mower stocks, few ask whether the association is causal or not. The aim is to use the association, not understand it—to be right about the market, not necessarily to be right for the right reasons.


The Data Journalism Handbook by Jonathan Gray, Lucy Chambers, Liliana Bounegru

Amazon Web Services, barriers to entry, bioinformatics, business intelligence, carbon footprint, citizen journalism, correlation does not imply causation, crowdsourcing, data science, David Heinemeier Hansson, eurozone crisis, fail fast, Firefox, Florence Nightingale: pie chart, game design, Google Earth, Hans Rosling, high-speed rail, information asymmetry, Internet Archive, John Snow's cholera map, Julian Assange, linked data, moral hazard, MVC pattern, New Journalism, openstreetmap, Ronald Reagan, Ruby on Rails, Silicon Valley, social graph, SPARQL, text mining, Wayback Machine, web application, WikiLeaks

Or you can divide the data subjects into groups: Analysis by categories “Councils run by the Purple Party spend 50% more on paper clips than those controlled by the Yellow Party.” Or you can relate factors numerically: Association “Councils run by politicians who have received donations from stationery companies spend more on paper clips, with spending increasing on average by £100 for each pound donated.” But, of course, always remember that correlation and causation are not the same thing. So if you’re investigating paper clip spending, are you also getting the following figures? Total spending to provide context? Geographical/historical/other breakdowns to provide comparative data? The additional data you need to ensure comparisons are fair, such as population size?


Thinking with Data by Max Shron

business intelligence, Carmen Reinhart, correlation does not imply causation, data science, Growth in a Time of Debt, iterative process, Kenneth Rogoff, randomized controlled trial, Richard Feynman, statistical model, The Design of Experiments, the scientific method

Physical examples provide us with our intuition for what cause and effect mean. They are the paragons of the phenomenon of causation, but unfortunately, outside of some particularly intensive engineering or scientific disciplines, they rarely arise in the practical business of data work. No matter how many caveats we may put into a report about correlation not implying causation, people will interpret arguments causally. Human beings make stories that will help them decide how to act. It is a sensible instinct. People want analysis with causal power. We can ignore their needs and hide behind the difficulty of causal analysis—or we can come to terms with the fact that causal analysis is necessary, and then figure out how to do it properly and when it truly does not apply.


pages: 50 words: 13,399

The Elements of Data Analytic Style by Jeff Leek

correlation does not imply causation, data science, Netflix Prize, p-value, pattern recognition, Ronald Coase, statistical model

The goal is to not only understand that there is an effect, but how that effect operates. An example of a mechanistic analysis is analyzing data on how wing design changes air flow over a wing, leading to decreased drag. Outside of engineering, mechanistic data analysis is extremely challenging and rarely undertaken. 2.8 Common mistakes 2.8.1 Correlation does not imply causation Interpreting an inferential analysis as causal. Most data analyses involve inference or prediction. Unless a randomized study is performed, it is difficult to infer why there is a relationship between two variables. Some great examples of correlations that can be calculated but are clearly not causally related appear at http://tylervigen.com/ (Figure 2.2).


pages: 270 words: 73,485

Hubris: Why Economists Failed to Predict the Crisis and How to Avoid the Next One by Meghnad Desai

"Robert Solow", 3D printing, Alan Greenspan, bank run, banking crisis, Bear Stearns, Berlin Wall, Big bang: deregulation of the City of London, Bretton Woods, BRICs, British Empire, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, correlation coefficient, correlation does not imply causation, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, David Ricardo: comparative advantage, deindustrialization, demographic dividend, Eugene Fama: efficient market hypothesis, eurozone crisis, experimental economics, Fall of the Berlin Wall, financial innovation, Financial Instability Hypothesis, floating exchange rates, full employment, German hyperinflation, Gunnar Myrdal, Home mortgage interest deduction, imperial preference, income inequality, inflation targeting, invisible hand, Isaac Newton, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, laissez-faire capitalism, liquidity trap, Long Term Capital Management, market bubble, market clearing, means of production, Mexican peso crisis / tequila crisis, mortgage debt, Myron Scholes, negative equity, Northern Rock, oil shale / tar sands, oil shock, open economy, Paul Samuelson, Phillips curve, Post-Keynesian economics, price stability, purchasing power parity, pushing on a string, quantitative easing, reserve currency, rising living standards, risk/return, Robert Shiller, Ronald Reagan, savings glut, secular stagnation, seigniorage, Silicon Valley, Simon Kuznets, The Chicago School, The Great Moderation, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Wealth of Nations by Adam Smith, Tobin tax, too big to fail, women in the workforce

(i) Clayton Act (i) Clinton Administration (i) closed economy (i), (ii), (iii), (iv), (v), (vi) Cobb, Charles (i) Cobb-Douglas Production Function (i), (ii) coincidence, vs.causation (i) Cold War (i) collateralized debt obligations (CDO) (i) colonization (i) Combinations (trade unions), as harmful (i) Committee on the Bank of England Charter (i) commodity markets price rises (i) regulation (i) Common Market (i) communications, advances in (i), (ii) companies, collapse of (i) comparative advantage (i) compatibility microeconomics/macroeconomics (i), (ii), (iii) unique static equilibrium/moving data (i) competition and efficiency (i) imperfect (i) theory of (Marshall) (i) computer technology development of (i), (ii); see also technological innovations stock markets (i) confidence, rise and fall (i) conflicting interests (i), (ii) Connally, John (i) consols (i) consumer credit (i) consumption function (i), (ii) contagion (i), (ii) control of money supply (i) convertibility (i) cooperation (i) correlation/coincidence, vs. causation (i) corruption (i) Countrywide Financial (i) Cournot, Antoine Augustin (i) Cowles, Alfred (i) Cowles Foundation (i) creative destruction (i) credit business dependence (i) cheap (i) as driver of investment (i) credit cards (i) credit default swaps (CDS) (i) crises beginnings of (i) developing countries (i) Juglar’s theory (i) Mexican (i) proliferation (i) as recurrent (i), (ii) as regular occurrences (i) ten year pattern (i) unpredictability (i) crisis of 1825 (i) crisis of profitability (i) Crosland, Anthony (i) The Future of Socialism (i) currency, convertibility (i) depreciation (i) pegging (i), (ii) cycles (i) banking system as root (i) combinations of (i) Goodwin (i), (ii) Juglar’s study (i) Keynes on (i) long (i) loss of interest in (i) Marx’s theories (i), (ii) measuring (i) origins (i) random events (i) reproduction by Keynesian models (i) rocking horse analogy (i) short (i) Wicksell’s theory (i) see also Frisch; Kondratieff cycles debit cards (i) Debreu, Gerard (i), (ii) debt crises (i) easy availability (i) levels (i) see also government debt debt-fueled boom (i) debts brokers (i) farmers’ (i) post-World War II (i) purchase of (i) decisions, patterns (i) deficits, endemic (i) deflation (i) deindustrialization (i), (ii) Deism (i) demand, factors in (i) demographics (i) demutualization (i) depreciation (i) advocacy of (i) Ricardo’s theory (i) value of goods (i) deregulation, banking (i) derivatives (i), (ii) Deserted Village, The (Oliver Goldsmith) (i) deutschmark (i) developing countries, Wicksellian boom (i) disequilibrium dynamic (i), (ii), (iii), (iv) stock (i) system, capitalism as (i) tradition (i) displacement effect, technological innovations (i) division of knowledge (i) division of labor (i), (ii) dollar purchasing power (i) as reserve currency (i), (ii) dollar exchange standard (i), (ii) dot.com boom (i) double deficits (i) Douglas, Paul (i), (ii) Dow Jones (i) Duménil, Gerard (i) durable goods (i) Dutch Disease (i) dynamic stochastic general equilibrium (DSGE) models (i), (ii) econometric modeling (i), (ii) Econometric Society (i), (ii) econometrics (i), (ii) economic activity, shift (i) economic analysis, applicability (i) economic cycles (i) Marx/Engels (i) see also Kondratieff cycles economic data, proliferation (i) economic growth, problems of (i) economic policy, activism (i) economic sectors, conflicting interests (i), (ii) economic slump, post-World War I (i) economic stagnation (i) economic theory (i) and individual lives (i) economic trajectories (i) economic vocabulary (i), (ii), (iii) economics background to (i) celebrated (i) changing scope of (i) as dismal science (i) professionalization (i) teaching of (i) “Economics and Knowledge” (Hayek) (i) economies, interconnections (i) economies of scale (i) economists, research methods (i) economy changing nature of (i) equilibrium/disequilibrium (i) visions of (i) efficiency, use of term (i) efficient market hypothesis (EMH) (i), (ii), (iii) Eisenhower, Dwight D.


pages: 719 words: 181,090

Site Reliability Engineering: How Google Runs Production Systems by Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Richard Murphy

Air France Flight 447, anti-pattern, barriers to entry, business intelligence, business process, Checklist Manifesto, cloud computing, combinatorial explosion, continuous integration, correlation does not imply causation, crowdsourcing, database schema, defense in depth, DevOps, en.wikipedia.org, fail fast, fault tolerance, Flash crash, George Santayana, Google Chrome, Google Earth, information asymmetry, job automation, job satisfaction, Kubernetes, linear programming, load shedding, loose coupling, meta-analysis, microservices, minimum viable product, MVC pattern, OSI model, performance metric, platform as a service, revision control, risk tolerance, side project, six sigma, the scientific method, Toyota Production System, trickle-down economics, warehouse automation, web application, zero day

The third trap is a set of logical fallacies that can be avoided by remembering that not all failures are equally probable—as doctors are taught, “when you hear hoofbeats, think of horses not zebras.”4 Also remember that, all things being equal, we should prefer simpler explanations.5 Finally, we should remember that correlation is not causation:6 some correlated events, say packet loss within a cluster and failed hard drives in the cluster, share common causes—in this case, a power outage, though network failure clearly doesn’t cause the hard drive failures nor vice versa. Even worse, as systems grow in size and complexity and as more metrics are monitored, it’s inevitable that there will be events that happen to correlate well with other events, purely by coincidence.7 Understanding failures in our reasoning process is the first step to avoiding them and becoming more effective in solving problems.

Diskerase example, Recommendations focus on reliability, Reliability Is the Fundamental Feature Google's approach to, The Value for Google SRE hierarchy of automation classes, A Hierarchy of Automation Classes recommendations for enacting, Recommendations specialized application of, The Inclination to Specialize use cases for, The Use Cases for Automation-A Hierarchy of Automation Classes automation tools, Testing Scalable Tools autonomous systems, The Evolution of Automation at Google Auxon case study, Auxon Case Study: Project Background and Problem Space-Our Solution: Intent-Based Capacity Planning, Introduction to Auxon-Introduction to Auxon availability, Indicators, Choosing a Strategy for Superior Data Integrity(see also service availability) availability table, Availability Table B B4 network, Hardware backend servers, Our Software Infrastructure, Load Balancing in the Datacenter backends, fake, Production Probes backups (see data integrity) Bandwidth Enforcer (BwE), Networking barrier tools, Testing Scalable Tools, Testing Disaster, Distributed Coordination and Locking Services batch processing pipelines, First Layer: Soft Deletion batching, Eliminate Batch Load, Batching, Drawbacks of Periodic Pipelines in Distributed Environments Bazel, Building best practicescapacity planning, Capacity Planning for change management, Change Management error budgets, Error Budgets failures, Fail Sanely feedback, Introducing a Postmortem Culture for incident management, In Summary monitoring, Monitoring overloads and failure, Overloads and Failure postmortems, Google’s Postmortem Philosophy-Collaborate and Share Knowledge, Postmortems reward systems, Introducing a Postmortem Culture role of release engineers in, The Role of a Release Engineer rollouts, Progressive Rollouts service level objectives, Define SLOs Like a User team building, SRE Teams bibliography, Bibliography Big Data, Origin of the Pipeline Design Pattern Bigtable, Storage, Target level of availability, Bigtable SRE: A Tale of Over-Alerting bimodal latency, Bimodal latency black-box monitoring, Definitions, Black-Box Versus White-Box, Black-Box Monitoring blameless cultures, Google’s Postmortem Philosophy Blaze build tool, Building Blobstore, Storage, Choosing a Strategy for Superior Data Integrity Borg, Hardware-Managing Machines, Borg: Birth of the Warehouse-Scale Computer-Borg: Birth of the Warehouse-Scale Computer, Drawbacks of Periodic Pipelines in Distributed Environments Borg Naming Service (BNS), Managing Machines Borgmon, The Rise of Borgmon-Ten Years On…(see also time-series monitoring) alerting, Monitoring and Alerting, Alerting configuration, Maintaining the Configuration rate() function, Rule Evaluation rules, Rule Evaluation-Rule Evaluation sharding, Sharding the Monitoring Topology timeseries arena, Storage in the Time-Series Arena vectors, Labels and Vectors-Labels and Vectors break-glass mechanisms, Expect Testing Fail build environments, Creating a Test and Build Environment business continuity, Ensuring Business Continuity Byzantine failures, How Distributed Consensus Works, Number of Replicas C campuses, Hardware canarying, Motivation for Error Budgets, What we learned, Canary test, Gradual and Staged Rollouts CAP theorem, Managing Critical State: Distributed Consensus for Reliability CAPA (corrective and preventative action), Postmortem Culture capacity planningapproaches to, Practices best practices for, Capacity Planning Diskerase example, Recommendations distributed consensus systems and, Capacity and Load Balancing drawbacks of "queries per second", The Pitfalls of “Queries per Second” drawbacks of traditional plans, Brittle by nature further reading on, Practices intent-based (see intent-based capacity planning) mandatory steps for, Demand Forecasting and Capacity Planning preventing server overload with, Preventing Server Overload product launches and, Capacity Planning traditional approach to, Traditional Capacity Planning cascading failuresaddressing, Immediate Steps to Address Cascading Failures-Eliminate Bad Traffic causes of, Causes of Cascading Failures and Designing to Avoid Them-Service Unavailability defined, Addressing Cascading Failures, Capacity and Load Balancing factors triggering, Triggering Conditions for Cascading Failures overview of, Closing Remarks preventing server overload, Preventing Server Overload-Always Go Downward in the Stack testing for, Testing for Cascading Failures-Test Noncritical Backends(see also overload handling) change management, Change Management(see also automation) change-induced emergencies, Change-Induced Emergency-What we learned changelists (CLs), Our Development Environment Chaos Monkey, Testing Disaster checkpoint state, Testing Disaster cherry picking tactic, Hermetic Builds Chubby lock service, Lock Service, System Architecture Patterns for Distributed Consensusplanned outage, Objectives, SLOs Set Expectations client tasks, Load Balancing in the Datacenter client-side throttling, Client-Side Throttling clients, Our Software Infrastructure clock drift, Managing Critical State: Distributed Consensus for Reliability Clos network fabric, Hardware cloud environmentdata integrity strategies, Choosing a Strategy for Superior Data Integrity, Challenges faced by cloud developers definition of data integrity in, Data Integrity’s Strict Requirements evolution of applications in, Choosing a Strategy for Superior Data Integrity technical challenges of, Requirements of the Cloud Environment in Perspective clustersapplying automation to turnups, Soothing the Pain: Applying Automation to Cluster Turnups-Service-Oriented Cluster-Turnup cluster management solution, Drawbacks of Periodic Pipelines in Distributed Environments defined, Hardware code samples, Using Code Examples cognitive flow state, Cognitive Flow State cold caching, Slow Startup and Cold Caching colocation facilities (colos), Recommendations Colossus, Storage command posts, A Recognized Command Post communication and collaborationblameless postmortems, Collaborate and Share Knowledge case studies, Case Study of Collaboration in SRE: Viceroy-Case Study: Migrating DFP to F1 importance of, Conclusion with Outalator, Reporting and communication outside SRE team, Collaboration Outside SRE position of SRE in Google, Communication and Collaboration in SRE production meetings (see production meetings) within SRE team, Collaboration within SRE company-wide resilience testing, Practices compensation structure, Compensation computational optimization, Our Solution: Intent-Based Capacity Planning configuration management, Configuration Management, Change-Induced Emergency, Integration, Process Updates configuration tests, Configuration test consensus algorithmsEgalitarian Paxos, Stable Leaders Fast Paxos, Reasoning About Performance: Fast Paxos, The Use of Paxos improving performance of, Distributed Consensus Performance Multi-Paxos, Disk Access Paxos, How Distributed Consensus Works, Disk Access Raft, Multi-Paxos: Detailed Message Flow, Stable Leaders Zab, Stable Leaders(see also distributed consensus systems) consistencyeventual, Managing Critical State: Distributed Consensus for Reliability through automation, Consistency consistent hashing, Load Balancing at the Virtual IP Address constraints, Laborious and imprecise Consul, System Architecture Patterns for Distributed Consensus consumer services, identifying risk tolerance of, Identifying the Risk Tolerance of Consumer Services-Other service metrics continuous build and deploymentBlaze build tool, Building branching, Branching build targets, Building configuration management, Configuration Management deployment, Deployment packaging, Packaging Rapid release system, Continuous Build and Deployment, Rapid testing, Testing typical release process, Rapid contributors, Acknowledgments-Acknowledgments coroutines, Origin of the Pipeline Design Pattern corporate network security, Practices correctness guarantees, Workflow Correctness Guarantees correlation vs. causation, Theory costsavailability targets and, Cost, Cost direct, The Sysadmin Approach to Service Management of failing to embrace risk, Managing Risk indirect, The Sysadmin Approach to Service Management of sysadmin management approach, The Sysadmin Approach to Service Management CPU consumption, The Pitfalls of “Queries per Second”, CPU, Overload Behavior and Load Tests crash-fail vs. crash-recover algorithms, How Distributed Consensus Works cronat large scale, Running Large Cron building at Google, Building Cron at Google-Running Large Cron idempotency, Cron Jobs and Idempotency large-scale deployment of, Cron at Large Scale leader and followers, The leader overview of, Summary Paxos algorithm and, The Use of Paxos-Storing the State purpose of, Distributed Periodic Scheduling with Cron reliability applications of, Reliability Perspective resolving partial failures, Resolving partial failures storing state, Storing the State tracking cron job state, Tracking the State of Cron Jobs uses for, Cron cross-industry lessonsApollo 8, Preface comparative questions presented, Lessons Learned from Other Industries decision-making skills, Structured and Rational Decision Making-Structured and Rational Decision Making Google's application of, Conclusions industry leaders contributing, Meet Our Industry Veterans key themes addressed, Lessons Learned from Other Industries postmortem culture, Postmortem Culture-Postmortem Culture preparedness and disaster testing, Preparedness and Disaster Testing-Defense in Depth and Breadth repetitive work/operational overhead, Automating Away Repetitive Work and Operational Overhead current state, exposing, Examine D D storage layer, Storage dashboardsbenefits of, Why Monitor?


pages: 685 words: 203,949

The Organized Mind: Thinking Straight in the Age of Information Overload by Daniel J. Levitin

airport security, Albert Einstein, Amazon Mechanical Turk, Anton Chekhov, Bayesian statistics, big-box store, business process, call centre, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, delayed gratification, Donald Trump, en.wikipedia.org, epigenetics, Eratosthenes, Exxon Valdez, framing effect, friendly fire, fundamental attribution error, Golden Gate Park, Google Glasses, haute cuisine, How many piano tuners are there in Chicago?, impulse control, index card, indoor plumbing, information retrieval, information security, invention of writing, iterative process, jimmy wales, job satisfaction, Kickstarter, life extension, longitudinal study, meta-analysis, more computing power than Apollo, Network effects, new economy, Nicholas Carr, optical character recognition, Pareto efficiency, pattern recognition, phenotype, placebo effect, pre–internet, profit motive, randomized controlled trial, Rubik’s Cube, Salesforce, shared worldview, Sheryl Sandberg, Skype, Snapchat, social intelligence, statistical model, Steve Jobs, supply-chain management, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Turing test, ultimatum game, Wayback Machine, zero-sum game

This may be the case, but it’s also possible that the kinds of people who go to Harvard in the first place come from wealthy and supportive families and therefore might have been likely to obtain higher-paying jobs regardless of where they went to college. Childhood socioeconomic status has been shown to be a major quantity correlated with adult salaries. Correlation is not causation. Proving causation requires carefully controlled scientific experiments. Then there are truly spurious correlations—odd pairings of facts that have no relationship to each other and no third factor x linking them. For example, we could plot the relationship between the global average temperature over the past four hundred years and the number of pirates in the world and conclude that the drop in the number of pirates is caused by global warming.

The Gricean maxim of relevance implies that no one would construct such a graph (below) unless they felt these two were related, but this is where critical thinking comes in. The graph shows that they are correlated, but not that one causes the other. You could spin an ad hoc theory—pirates can’t stand heat, and so, as the oceans became warmer, they sought other employment. Examples such as this demonstrate the folly of failing to separate correlation from causation. It is easy to confuse cause and effect when encountering correlations. There is often that third factor x that ties together correlative observations. In the case of the decline in pirates being related to the increase in global warming, factor x might plausibly be claimed to be industrialization.


pages: 346 words: 89,180

Capitalism Without Capital: The Rise of the Intangible Economy by Jonathan Haskel, Stian Westlake

"Robert Solow", 23andMe, activist fund / activist shareholder / activist investor, Airbnb, Alan Greenspan, Albert Einstein, Alvin Toffler, Andrei Shleifer, bank run, banking crisis, Bernie Sanders, Big Tech, Brexit referendum, business climate, business process, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, cognitive bias, computer age, corporate governance, corporate raider, correlation does not imply causation, creative destruction, dark matter, Diane Coyle, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Glaeser, Elon Musk, endogenous growth, Erik Brynjolfsson, everywhere but in the productivity statistics, Fellow of the Royal Society, financial engineering, financial innovation, full employment, fundamental attribution error, future of work, Gini coefficient, Hernando de Soto, hiring and firing, income inequality, index card, indoor plumbing, intangible asset, Internet of things, Jane Jacobs, Jaron Lanier, job automation, Kanban, Kenneth Arrow, Kickstarter, knowledge economy, knowledge worker, laissez-faire capitalism, liquidity trap, low skilled workers, Marc Andreessen, Mother of all demos, Network effects, new economy, Ocado, open economy, patent troll, paypal mafia, Peter Thiel, pets.com, place-making, post-industrial society, Productivity paradox, quantitative hedge fund, rent-seeking, revision control, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Sand Hill Road, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, software patent, sovereign wealth fund, spinning jenny, Steve Jobs, sunk-cost fallacy, survivorship bias, tacit knowledge, tech billionaire, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Tim Cook: Apple, total factor productivity, Tyler Cowen, Tyler Cowen: Great Stagnation, urban planning, Vanguard fund, walkable city, X Prize, zero-sum game

The fact that there is a long-term increase in price suggests that, while good management practices improve firm performance (hence the long-term share price increase), equity markets undervalue the benefits of this type of intangible (since equity analysts should be able to recognize good management at the time the award is given, rather than waiting for its results to show up on the income statement). But, of course, correlation is not causation: just because a publicly listed firm invests less in R&D, training, or other intangibles does not mean it is being led astray by equity markets. Managers might choose to invest less because they know the investments available to them are unlikely to be profitable or, more narrowly, that they might be profitable for someone, but not necessarily for them.

Research by one of the authors together with Alan Hughes, Peter Goodridge, and Gavin Wallis suggests that extra investment by the UK government in research in universities increases national productivity by 20 percent (Haskel et al. 2015). (There were substantial swings in government support for universities over the 1990s and 2000s, and those ups and downs are well correlated with productivity ups and downs, with around a three-year lag.) As we have pointed out, correlation does not prove causation. For example, many universities are in economically fortunate areas. But does this mean having a good university raises local economic fortunes? Or do rich areas open universities? One needs a strategy to identify the causal link, if there is one, from university spending to local prosperity.


pages: 295 words: 89,430

Small Data: The Tiny Clues That Uncover Huge Trends by Martin Lindstrom

autonomous vehicles, Berlin Wall, big-box store, correlation does not imply causation, Edward Snowden, Fall of the Berlin Wall, land reform, Mikhail Gorbachev, Murano, Venice glass, Richard Florida, rolodex, self-driving car, Skype, Snapchat, Steve Jobs, Steven Pinker, too big to fail, urban sprawl

A source who works at Google once confessed to me that despite the almost 3 billion humans who are online,4 and the 70 percent of online shoppers who go onto Facebook daily,5 and the 300 hours of videos on YouTube (which is owned by Google) uploaded every minute,6 and the fact that 90 percent of all the world’s data has been generated over the last two years.7 Google ultimately has only limited information about consumers. Yes, search engines can detect unusual correlations (as opposed to causations). With 70 percent accuracy, my source tells me, software can assess how people feel based on the way they type, and the number of typos they make. With 79 percent precision, software can determine a user’s credit rating based on the degree to which they write in ALL CAPS. Yet even with all these stats, Google has come to realize it knows almost nothing about humans and what really drives us, and it is now bringing in consultants to do what small data researchers have been doing for decades.


pages: 56 words: 16,788

The New Kingmakers by Stephen O'Grady

AltaVista, Amazon Web Services, barriers to entry, cloud computing, correlation does not imply causation, crowdsourcing, David Heinemeier Hansson, DevOps, Hacker News, Jeff Bezos, Khan Academy, Kickstarter, Marc Andreessen, Mark Zuckerberg, Netflix Prize, Paul Graham, Ruby on Rails, Silicon Valley, Skype, software as a service, software is eating the world, Steve Ballmer, Steve Jobs, The future is already here, Tim Cook: Apple, Y Combinator

Google understood that developers are more likely to build for themselves—what’s referred to in the industry as “scratching their own itch”—Google made sure that several thousand developers motivated enough to attend their conference had an Android device to use for themselves. The statistics axiom that correlation does not prove causation certainly applies here, but it’s impossible not to notice the timing of that handset giveaway. On the day that Google sent all of those I/O attendees home happy, the number of Android devices being activated per day was likely in the low tens of thousands (Google hasn’t made this data available).


pages: 660 words: 141,595

Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost, Tom Fawcett

Albert Einstein, Amazon Mechanical Turk, big data - Walmart - Pop Tarts, bioinformatics, business process, call centre, chief data officer, Claude Shannon: information theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, data acquisition, data science, David Brooks, en.wikipedia.org, Erik Brynjolfsson, Gini coefficient, Helicobacter pylori, independent contractor, information retrieval, intangible asset, iterative process, Johann Wolfgang von Goethe, Louis Pasteur, Menlo Park, Nate Silver, Netflix Prize, new economy, p-value, pattern recognition, placebo effect, price discrimination, recommendation engine, Ronald Coase, selection bias, Silicon Valley, Skype, SoftBank, speech recognition, Steve Jobs, supply-chain management, Teledyne, text mining, The Signal and the Noise by Nate Silver, Thomas Bayes, transaction costs, WikiLeaks

The contents of these New Briefs varied, but because of their very similar form they clustered together: BRIEF-Apple releases Safari 3.1 BRIEF-Apple introduces ilife 2009 BRIEF-Apple announces iPhone 2.0 software beta BRIEF-Apple to offer movies on iTunes same day as DVD release BRIEF-Apple says sold one million iPhone 3G's in first weekend As we can see, some of these clusters are interesting and thematically consistent while others are not. Some are just collections of superficially similar text. There is an old cliché in statistics: Correlation is not causation, meaning that just because two things co-occur doesn’t mean that one causes another. A similar caveat in clustering could be: Syntactic similarity is not semantic similarity. Just because two things—particularly text passages—have common surface characteristics doesn’t mean they’re necessarily related semantically.

Data-Driven Causal Explanation and a Viral Marketing Example One important topic that we have only touched on in this book (in Chapter 2 and Chapter 11) is causal explanation from data. Predictive modeling is extremely useful for many business problems. However, the sort of predictive modeling that we have discussed so far is based on correlations rather than on knowledge of causation. We often want to look more deeply into a phenomenon and ask what influences what. We may want to do this simply to understand our business better, or we may want to use data to improve decisions about how to intervene to cause a desired outcome. Consider a detailed example.


pages: 527 words: 147,690

Terms of Service: Social Media and the Price of Constant Connection by Jacob Silverman

23andMe, 4chan, A Declaration of the Independence of Cyberspace, Aaron Swartz, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, basic income, Big Tech, Brian Krebs, California gold rush, call centre, cloud computing, cognitive dissonance, commoditize, context collapse, correlation does not imply causation, Credit Default Swap, crowdsourcing, data science, digital capitalism, disinformation, don't be evil, drone strike, Edward Snowden, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, hive mind, Ian Bogost, income inequality, independent contractor, informal economy, information retrieval, Internet of things, Jaron Lanier, jimmy wales, John Perry Barlow, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, late capitalism, license plate recognition, life extension, lifelogging, Lyft, Mark Zuckerberg, Mars Rover, Marshall McLuhan, mass incarceration, meta-analysis, Minecraft, move fast and break things, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, optical character recognition, payday loans, Peter Thiel, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, recommendation engine, rent control, RFID, ride hailing / ride sharing, Salesforce, self-driving car, sentiment analysis, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley ideology, Snapchat, social graph, social intelligence, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, TaskRabbit, technoutopianism, telemarketer, transportation-network company, Travis Kalanick, Turing test, Uber and Lyft, Uber for X, uber lyft, universal basic income, unpaid internship, women in the workforce, Y Combinator, you are the product, Zipcar

Both are in the data collection and targeting business, and Silicon Valley collects heaps of data which the NSA would love to have.* Silicon Valley is merely targeting consumers with ads and prompts and nudges that might get them to click or to buy something. They are bound together by common interests, philosophies, and methods. One of the main problems with Big Data is that it produces correlations but not causations. We learn that two things seem to be related—for example, that people with a specific set of personal characteristics are prone to depression or bad driving—but we don’t learn why. This is ironic given that Big Data is the ultimate fact-producing discipline: it promises answers, actionable ones.

., 21 banality problem on Facebook, 45–50 Barbrook, Richard, 1–3, 4, 250–51 Barlow, John Perry, 251–52 Beacon advertising platform, Facebook, 287 “Bed Intruder Song” (Gregory Brothers), 71 BehaviorMatrix, 39 Beliebers, 147–48 Bellow, Saul, 59 Benjamin, Walter, 267 Berger, John, 24 Bergus, Nick, 31–32 Berlusconi, Silvio, 211 Beyond Verbal, 40–41 Bieber, Justin, 147–48 Big Brother (reality TV show), 135 Big Data overview, 232, 313–14, 316 correlations without causations, 315 and ethics, 325–26 future of, 329–32 as information harvesting, 297 need for, 323 and patterns, 315 uses for, 316, 327–28 See also informational appetite Bilton, Nick, 34 Binder, Matt, 170–72, 173 Bing search engine, 195 biometric targeting tools, 305–6 Blanchard, Nathalie, 308–9 Bleacher Report, 125–28 BlinkLink app, 358 Blodget, Henry, 125 Bogost, Ian, 264 Booker, Cory, 104–5 BookVibe, 34 Boorstin, Daniel J., 67, 104 Boston Marathon bombing, 110–11, 113 Bosworth, Andrew, 25 bots overview, 38–39 and fraudulent ad companies, 97–98 influence scores, 194 recognizing a trend, 89–90 remote personal assistants, 42–43 as substitutes for individuals, 151 botting, 85–87, 88–89 Boyd, Danah, 168, 274, 284, 291, 315 Boy Kings, The (Losse), 6, 129, 142n Bradbury, Ray, 339–40 Brady, Tom, 126 Brandeis, Louis, 288–90 Brand Yourself, 213 Breaking Bad hashtag, 94 “Breaking News Consumer’s Handbook” (On the Media), 109 BRICKiPhone, 359–60 Britain, 144, 306, 314 Bucher, Taina, 200, 201 Burberry, 96–97 businesses.


pages: 470 words: 148,730

Good Economics for Hard Times: Better Answers to Our Biggest Problems by Abhijit V. Banerjee, Esther Duflo

"Robert Solow", 3D printing, accelerated depreciation, affirmative action, Affordable Care Act / Obamacare, air traffic controllers' union, Airbnb, basic income, Bernie Sanders, Big Tech, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, charter city, correlation does not imply causation, creative destruction, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, decarbonisation, Deng Xiaoping, Donald Trump, Edward Glaeser, en.wikipedia.org, endowment effect, energy transition, Erik Brynjolfsson, experimental economics, experimental subject, facts on the ground, fear of failure, financial innovation, George Akerlof, high net worth, immigration reform, income inequality, Indoor air pollution, industrial cluster, industrial robot, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), Jane Jacobs, Jean Tirole, Jeff Bezos, job automation, Joseph Schumpeter, junk bonds, labor-force participation, land reform, loss aversion, low skilled workers, manufacturing employment, Mark Zuckerberg, mass immigration, middle-income trap, Network effects, new economy, New Urbanism, non-tariff barriers, obamacare, offshore financial centre, open economy, Paul Samuelson, place-making, price stability, profit maximization, purchasing power parity, race to the bottom, RAND corporation, randomized controlled trial, Richard Thaler, ride hailing / ride sharing, Robert Gordon, Ronald Reagan, Savings and loan crisis, school choice, Second Machine Age, secular stagnation, self-driving car, shareholder value, short selling, Silicon Valley, smart meter, social graph, spinning jenny, Steve Jobs, Tax Reform Act of 1986, tech worker, technology bubble, The Chicago School, The Future of Employment, The Market for Lemons, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, total factor productivity, trade liberalization, transaction costs, trickle-down economics, universal basic income, urban sprawl, very high income, War on Poverty, women in the workforce, working-age population, Y2K

According to the World Inequality Database team, in 1978 the bottom 50 percent and the top 10 percent of the population both took home the same share of Chinese income (27 percent). The two shares starting diverging in 1978, with the poorest 50 percent taking less and less and the richest 10 percent taking more and more. By 2015, the top 10 percent received 41 percent of Chinese income, while the bottom 50 percent received 15 percent.19 Of course, correlation is not causation. Perhaps globalization per se did not cause the increase in inequality. Trade liberalizations almost never take place in a vacuum; in all these countries, trade reforms were part of a broader reform package. For example, the most drastic trade policy liberalization in Colombia in 1990 and 1991 coincided with changes in labor market regulation meant to substantially increase labor market flexibility.

There is no evidence the Reagan tax cuts, or the Clinton top marginal rate increase, or the Bush tax cuts, did anything to change the long-run growth rate.52 Of course, as the Republican Paul Ryan, former Speaker of the House of Representatives, pointed out, there is no evidence that they did not. Many other things were happening at the same time. Ryan painstakingly explained to a journalist why all of these things lined up to make tax increases look good and tax decreases look bad: I wouldn’t say that correlation is causation. I would say Clinton had the tech-productivity boom, which was enormous. Trade barriers were going down in the Clinton years. He had the peace dividend he was enjoying.… The economy in the Bush years, by contrast, had to cope with the popping of the technology bubble, 9/11, a couple of wars and the financial meltdown.… Some of this is just the timing, not the person.… Just as the Keynesians say the economy would have been worse without the stimulus [that Mr.


pages: 387 words: 106,753

Why Startups Fail: A New Roadmap for Entrepreneurial Success by Tom Eisenmann

Airbnb, Atul Gawande, autonomous vehicles, Ben Horowitz, Big Tech, bitcoin, blockchain, call centre, carbon footprint, Checklist Manifesto, cleantech, conceptual framework, coronavirus, corporate governance, correlation does not imply causation, COVID-19, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, Dean Kamen, Elon Musk, fail fast, fundamental attribution error, gig economy, Hyperloop, income inequality, inventory management, Iridium satellite, Jeff Bezos, Jeff Hawkins, Lean Startup, Lyft, Marc Andreessen, margin call, Mark Zuckerberg, minimum viable product, Network effects, nuclear winter, Oculus Rift, PalmPilot, Paul Graham, performance metric, Peter Pan Syndrome, Peter Thiel, Richard Thaler, ride hailing / ride sharing, risk/return, Salesforce, Sam Altman, Sand Hill Road, side project, Silicon Valley, Silicon Valley startup, Skype, social graph, software as a service, speech recognition, stealth mode startup, Steve Jobs, two-sided market, Uber and Lyft, Uber for X, uber lyft, We wanted flying cars, instead we got 140 characters, WeWork, Y Combinator, young professional

In the text below, for variables in my regression model that are statistically significant predictors of low valuation with at least 90 percent confidence, I’ll show—bolded—the predicted probability of observing a low valuation outcome as the focal variable is ranged from its lowest possible survey response to the highest possible response, while holding the level of all other independent variables constant at their respective sample means. When gauging the impact of these variables, note that the baseline predicted probability of a low valuation (with all variables at their sample mean) is 10 percent. When interpreting results, remember that correlation does not always imply causation. If a certain factor—say, a strong company culture—is more often associated with high valuation startups than low valuation counterparts, it might be true that a strong culture leads to strong performance. Or, the reverse might be true. The statistical techniques I use here cannot determine causation.

Not surprisingly, they were also significantly less likely to say their startup had a strong company culture. In multivariate analysis, moving from “much weaker” to “much stronger” when assessing the strength of one’s company culture relative to that of peer startups reduced the probability of low valuation sharply, from 23 percent to 6 percent. As noted above, however, this correlation does not necessarily imply causation. A weak culture might be a consequence of the startup’s problems, rather than the cause of its struggles. In recruiting, low valuation startups were somewhat more likely to put too much emphasis on both skill and attitude. In multivariate analysis, the penchant to overemphasize skills had a small but statistically significant impact.

In multivariate analysis, raising less than 75 percent of a startup’s initial funding goal resulted in an 18 percent predicted probability of low valuation, compared to 7 percent for startups that raised more than 125 percent of their initial goal. However, this is another variable for which correlation may not imply causation. Some startups may fail to meet their fundraising goals because it’s evident to investors that they’ve enlisted a weak team or targeted a bad idea—or both. These startups may ultimately be shut down due to a lack of capital, but the root cause of their failure was a “jockey and/or horse” problem.


pages: 231 words: 73,818

The Achievement Habit: Stop Wishing, Start Doing, and Take Command of Your Life by Bernard Roth

Albert Einstein, Build a better mousetrap, Burning Man, cognitive bias, correlation does not imply causation, deskilling, do what you love, fear of failure, functional fixedness, Mahatma Gandhi, Mark Zuckerberg, school choice, Silicon Valley, The Wealth of Nations by Adam Smith, zero-sum game

A woman comes up to him after some time and says, “Pardon me, sir, why are you snapping your fingers?” He replies, “I am keeping the tigers away.” She says, “Sir, except for the zoo, there’s not a tiger for thousands of miles.” “Pretty effective, isn’t it?” he says. This joke uses what is called a causal fallacy. The fallacy comes because the finger snapper mistakenly believes that correlation implies causation. This is just one of several logical fallacies in which two events that occur at the same time are taken to have a cause-and-effect relationship. This version of the fallacy is also known as cum hoc ergo propter hoc (Latin for “with this, therefore because of this”) or, simply, false cause.


pages: 589 words: 69,193

Mastering Pandas by Femi Anthony

Amazon Web Services, Bayesian statistics, correlation coefficient, correlation does not imply causation, data science, Debian, en.wikipedia.org, Internet of things, natural language processing, p-value, random walk, side project, statistical model, Thomas Bayes

We can then use this relationship to try and predict the value of one set of variables from the other; this is termed as regression. Correlation The statistical dependence expressed in a correlation relationship does not imply a causal relationship between the two variables; the famous line on this is "Correlation does not imply Causation". Thus, correlation between two variables or datasets implies just a casual rather than a causal relationship or dependence. For example, there is a correlation between the amount of ice cream purchased on a given day and the weather. For more information on correlation and dependency, refer to http://en.wikipedia.org/wiki/Correlation_and_dependence.


pages: 233 words: 75,712

In Defense of Global Capitalism by Johan Norberg

anti-globalists, Asian financial crisis, capital controls, clean water, correlation does not imply causation, creative destruction, Deng Xiaoping, Edward Glaeser, Gini coefficient, half of the world's population has never made a phone call, Hernando de Soto, illegal immigration, income inequality, income per capita, informal economy, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", Joseph Schumpeter, Kenneth Rogoff, land reform, Lao Tzu, liberal capitalism, market fundamentalism, Mexican peso crisis / tequila crisis, Naomi Klein, new economy, open economy, prediction markets, profit motive, race to the bottom, rising living standards, Silicon Valley, Simon Kuznets, structural adjustment programs, The Wealth of Nations by Adam Smith, Tobin tax, trade liberalization, trade route, transaction costs, trickle-down economics, Tyler Cowen, union organizing, zero-sum game

Criticism has been leveled at this type of regression analysis, which is based on statistics from many economies and tries to control for other factors that can affect economic outcomes, because of the many problems of measurement that such analysis involves. Coping with enormous masses of data is always a problem. Where exactly is the line between open and closed economies? How does one distinguish between correlation and causation? How can the direction of causation be established? Consider, after all, that it is common for countries implementing free trade to also introduce other liberal reforms, such as protection for property rights, reduced inflation, and balanced budgets. That makes it hard to separate the effects of one policy from the effects of another.8 The problems of measurement are real ones, and results of this kind always have to be taken with a grain of salt, but it remains interesting that, with so very few exceptions, those studies point to great advantages with free trade.


pages: 265 words: 74,000

The Numerati by Stephen Baker

Berlin Wall, Black Swan, business process, call centre, correlation does not imply causation, Drosophila, full employment, illegal immigration, index card, information security, Isaac Newton, job automation, job satisfaction, junk bonds, McMansion, Myron Scholes, natural language processing, PageRank, personalized medicine, recommendation engine, RFID, Silicon Valley, Skype, statistical model, surveillance capitalism, Watson beat the top human players on Jeopardy!

Sifry goes on at length about the dangers of predicting people's behavior based on statistical correlations. "Let's say that according to my analytics, you said that Mission Impossible III was no good and that you can't wait to see Prairie Home Companion," he says. "I can't assume from that that you're an NPR listener. That's where you get into trouble." That's mistaking correlation for causation, he says. It's common among data miners—and most other humans. How many times have you heard people say, "They always do that..."? For Kaushansky, putting his skateboarding friend and a few others in the wrong tribes may not turn out to be too serious. That's why advertising and marketing are such wonderful testing grounds for the Numerati.


pages: 290 words: 76,216

What's Wrong With Economics: A Primer for the Perplexed by Robert Skidelsky

"Robert Solow", additive manufacturing, agricultural Revolution, Black Swan, Bretton Woods, business cycle, Cass Sunstein, central bank independence, cognitive bias, conceptual framework, Corn Laws, corporate social responsibility, correlation does not imply causation, creative destruction, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, disruptive innovation, Donald Trump, Dr. Strangelove, full employment, George Akerlof, George Santayana, global supply chain, global village, Gunnar Myrdal, happiness index / gross national happiness, hindsight bias, Hyman Minsky, income inequality, index fund, inflation targeting, information asymmetry, Internet Archive, invisible hand, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kenneth Arrow, knowledge economy, labour market flexibility, loss aversion, Mahbub ul Haq, Mark Zuckerberg, market clearing, market friction, market fundamentalism, Martin Wolf, means of production, Modern Monetary Theory, moral hazard, paradox of thrift, Pareto efficiency, Paul Samuelson, Philip Mirowski, Phillips curve, precariat, price anchoring, principal–agent problem, rent-seeking, Richard Thaler, road to serfdom, Robert Shiller, Ronald Coase, shareholder value, Silicon Valley, Simon Kuznets, sunk-cost fallacy, survivorship bias, technoutopianism, The Chicago School, The Market for Lemons, The Nature of the Firm, the scientific method, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Thorstein Veblen, Tragedy of the Commons, transaction costs, transfer pricing, Vilfredo Pareto, Washington Consensus, Wolfgang Streeck, zero-sum game

To regret that is to regret the growth itself. It is to hold, in effect, that it is better for everyone . . . to remain equally poor. [This] seems to me . . . morally indefensible and practically untenable . . . This debate illustrates very well why economics is not a hard science. At issue is correlation versus causation (if two or more events run in parallel, which, if either, causes the other?), reliability of the data (how much trust can you put in official statistics?), the ideological complexion of economic models (is the world economy best understood as a unitary or binary system?), universal versus contingent truths (do different economic structures have the same laws of development?)


pages: 276 words: 81,153

Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives by David Sumpter

affirmative action, algorithmic bias, Bernie Sanders, Brexit referendum, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data science, disinformation, don't be evil, Donald Trump, Elon Musk, Filter Bubble, Google Glasses, illegal immigration, Jeff Bezos, job automation, Kenneth Arrow, Loebner Prize, Mark Zuckerberg, meta-analysis, Minecraft, Nate Silver, natural language processing, Nelson Mandela, p-value, prediction markets, random walk, Ray Kurzweil, Robert Mercer, selection bias, self-driving car, Silicon Valley, Skype, Snapchat, speech recognition, statistical model, Stephen Hawking, Steve Bannon, Steven Pinker, The Signal and the Noise by Nate Silver, traveling salesman, Turing test

In fact, the regression model I fitted to Facebook data does not reveal anything about the 76 per cent of people who didn’t register their political allegiance. While the data shows us that Democrats tend to like Harry Potter, it doesn’t necessarily tell us that other Harry Potter fans like the Democrats. This is the classic problem inherent to all statistical analyses; of potentially confusing correlation with causation. A second limitation relates to the number of ‘likes’ needed to make predictions. The regression model only works when a person has made more than 50 ‘likes’ and, to make really reliable predictions, a few hundred ‘likes’ are required. In the Facebook data set, only 18 per cent of users ‘liked’ more than 50 sites.


pages: 309 words: 81,975

Brave New Work: Are You Ready to Reinvent Your Organization? by Aaron Dignan

"side hustle", activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, Black Swan, blockchain, Buckminster Fuller, Burning Man, butterfly effect, cashless society, Clayton Christensen, clean water, cognitive bias, cognitive dissonance, content marketing, corporate governance, corporate social responsibility, correlation does not imply causation, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, David Heinemeier Hansson, deliberate practice, DevOps, disruptive innovation, don't be evil, Elon Musk, endowment effect, Ethereum, ethereum blockchain, financial engineering, Frederick Winslow Taylor, fulfillment center, future of work, gender pay gap, Geoffrey West, Santa Fe Institute, gig economy, Goodhart's law, Google X / Alphabet X, hiring and firing, hive mind, holacracy, impact investing, income inequality, information asymmetry, Internet of things, Jeff Bezos, job satisfaction, Kanban, Kevin Kelly, Kickstarter, Lean Startup, loose coupling, loss aversion, Lyft, Marc Andreessen, Mark Zuckerberg, minimum viable product, new economy, Paul Graham, race to the bottom, remote working, Richard Thaler, Salesforce, scientific management, shareholder value, Silicon Valley, six sigma, smart contracts, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, source of truth, Stanford marshmallow experiment, Steve Jobs, TaskRabbit, The future is already here, the High Line, too big to fail, Toyota Production System, Tragedy of the Commons, uber lyft, universal basic income, WeWork, Y Combinator, zero-sum game

Rather than hold these “steps” lightly, executives tend to view them as best practice and attempt to implement them linearly and authoritatively, often with lackluster results. Kotter himself updated the process in 2014, acknowledging that the steps should really be done concurrently and continuously. The problem isn’t that we use models and frameworks to better understand change (although we need to be careful about correlation versus causation when it comes to defining what “works”). The problem is that we mistake the organization for an ordered system. And so we oversimplify. As a result, we tend to force whatever is happening in the system—in the hearts and minds of our colleagues—into that framework. We start saying things such as “You’re frozen right now, Michael, and we’re in the ‘unfreeze’ part of our change process, so . . . we’re going to need you to change your attitude.”


pages: 442 words: 85,640

This Book Could Fix Your Life: The Science of Self Help by New Scientist, Helen Thomson

caloric restriction, caloric restriction, coronavirus, correlation does not imply causation, COVID-19, David Attenborough, delayed gratification, Donald Trump, Elon Musk, Flynn Effect, George Floyd, global pandemic, hedonic treadmill, job satisfaction, Kickstarter, meta-analysis, microbiome, placebo effect, publication bias, randomized controlled trial, risk tolerance, selective serotonin reuptake inhibitor (SSRI), Sheryl Sandberg, Steve Jobs, sunk-cost fallacy, survivorship bias, Walter Mischel

If that wasn’t enough, when the researchers looked for signs of P. gingivalis in the brains of healthy people, they found some, but at low levels. This supports the theory that P. gingivalis doesn’t get into the brain as a result of Alzheimer’s – but might be the cause. As a well-informed reader, you’ll know that caution is still required in saying that correlation implies causation: not every link between two factors means the one causes the other, and both could have an entirely separate cause. Our history of failed theories and drugs associated with Alzheimer’s is perhaps a further reason to stay cautious. But in this case several experiments are converging to suggest that gum disease really might be behind the disease.


pages: 290 words: 82,220

Four Lost Cities: A Secret History of the Urban Age by Annalee Newitz

biofilm, clean water, correlation does not imply causation, COVID-19, David Graeber, European colonialism, Ferguson, Missouri, Geoffrey West, Santa Fe Institute, Jane Jacobs, mass immigration, megacity, rent control, the built environment, trade route, urban planning, urban sprawl

Unlike the residential mounds excavated by Carter and her colleagues, these mounds aren’t packed with ceramics and food remains. They are just mounds, clearly the foundations for an elevated structure or structures. Their locations suggest that they may have been related to the city’s waterworks, but of course correlation does not equal causation. The coils and mound fields are reminders of how much we still don’t understand about how the ancient Khmer built their cities. Suryavarman II and his predecessors were nothing without all those commoners and khñum cutting sandstone, smelting iron, harvesting rice, and shipping it back to the capital.

Chapter 12: Deliberate Abandonment 1. Samuel E. Munoz et al., “Cahokia’s Emergence and Decline Coincided with Shifts of Flood Frequency on the Mississippi River,” Proceedings of the National Academy of Sciences 112, no. 20 (May 2015): 6319–24. 2. Sarah E. Baires, Melissa R. Baltus, and Meghan E. Buchanan, “Correlation Does Not Equal Causation: Questioning the Great Cahokia Flood,” Proceedings of the National Academy of Sciences 112, no. 29 (July 2015): E3753. 3. Andrea Hunter, “Ancestral Osage Geography,” in Andrea A. Hunter, James Munkres, and Barker Fariss, Osage Nation NAGPRA Claim for Human Remains Removed from the Clarksville Mound Group (23PI6), Pike County, Missouri (Pawhuska, OK: Osage Nation Historic Preservation Office, 2013), 1–60, https://www.osagenation-nsn.gov/who-we-are/historic-preservation/osage-cultural-history. 4.


pages: 687 words: 165,457

Exercised: The Science of Physical Activity, Rest and Health by Daniel Lieberman

A. Roger Ekirch, active measures, caloric restriction, caloric restriction, clean water, clockwatching, Coronary heart disease and physical activity of work, correlation does not imply causation, COVID-19, Donald Trump, epigenetics, Exxon Valdez, George Santayana, hygiene hypothesis, impulse control, indoor plumbing, Kickstarter, libertarian paternalism, longitudinal study, meta-analysis, microbiome, mouse model, phenotype, placebo effect, publication bias, randomized controlled trial, Ronald Reagan, selective serotonin reuptake inhibitor (SSRI), Steven Pinker, twin studies, two and twenty, working poor

Many of these studies are epidemiological; they look for associations between, say, health and physical activity in large samples of individuals. For example, hundreds of studies have looked for correlations between heart disease, exercise habits, and factors like age, sex, and income. These analyses reveal correlations, not causation. There has also been no lack of experiments that randomly assign people (most often college students) or mice to contrasting treatment groups for short periods of time to measure the effects of particular variables on particular outcomes. Hundreds of such studies have looked, for instance, at the effects of varying doses of exercise on blood pressure or cholesterol levels.

Yet in 2002, the sleep world was rocked by a massive study by Daniel Kripke and colleagues that examined the health records and sleep patterns of more than one million Americans.30 According to these data, Americans who slept eight hours a night had 12 percent higher death rates than those who slept six and a half to seven and a half hours. In addition, heavy sleepers who reported more than eight and a half hours and light sleepers who reported less than four hours had 15 percent higher death rates. Critics pounced on the study’s flaws: the sleep data were self-reported; people who sleep a lot may already be sick; correlation is not causation. Yet since then, numerous studies using better data and sophisticated methods to correct for factors like age, illness, and income have confirmed that people who sleep about seven hours tend to live longer than those who sleep more or less.31 In no study is eight hours optimal, and in most of the studies people who got more than seven hours had shorter life spans than those who got less than seven hours (an unresolved issue, however, is whether it would be beneficial for long sleepers to reduce their sleep time).


pages: 451 words: 103,606

Machine Learning for Hackers by Drew Conway, John Myles White

call centre, centre right, correlation does not imply causation, data science, Debian, Erdős number, Nate Silver, natural language processing, Netflix Prize, p-value, pattern recognition, Paul Erdős, recommendation engine, social graph, SpamAssassin, statistical model, text mining, the scientific method, traveling salesman

This is a general fact about how correlations work, so you can always use linear regression to help you envision exactly what it means for two variables to be correlated. Because correlation is just a measure of how linear the relationship between two variables is, it tells us nothing about causality. This leads to the maxim that “correlation is not causation.” Nevertheless, it’s very important to know whether two things are correlated if you want to use one of them to make predictions about the other. That concludes our introduction to linear regression and the concept of correlation. In the next chapter, we’ll show how to run much more sophisticated regression models that can handle nonlinear patterns in data and simultaneously prevent overfitting.


pages: 371 words: 109,320

News and How to Use It: What to Believe in a Fake News World by Alan Rusbridger

airport security, basic income, Bellingcat, Big Tech, Boris Johnson, Brexit referendum, call centre, Chelsea Manning, citizen journalism, Climategate, cognitive dissonance, coronavirus, correlation does not imply causation, COVID-19, Credit Default Swap, cross-subsidies, crowdsourcing, disinformation, Dominic Cummings, Donald Trump, Edward Snowden, Filter Bubble, future of journalism, George Floyd, ghettoisation, global pandemic, Google Earth, hive mind, housing crisis, Howard Rheingold, illegal immigration, Intergovernmental Panel on Climate Change (IPCC), Jeff Bezos, Jeffrey Epstein, Johann Wolfgang von Goethe, Julian Assange, Kickstarter, Mark Zuckerberg, Murray Gell-Mann, Narrative Science, Neil Kinnock, Nelson Mandela, New Journalism, Nicholas Carr, offshore financial centre, profit motive, publication bias, Seymour Hersh, Snapchat, Steve Bannon, tech baron, the scientific method, universal basic income, WikiLeaks, yellow journalism

We should also take care whether too much attention is paid to the sample size, in these days of hype around ‘big data’. Big isn’t always better – a smaller balanced sample may provide better insights than a large skewed one. Is there someone in the newsroom who can properly interrogate the polling company and its methods? Who understands correlation does not mean causation? How many reporters might mix up their millions with their billions? How do you make billions meaningful to anyone? (One answer: try dividing a huge sum by the number of items they relate to. The NHS annual budget of £134 billion sounds vast, but this relates to £2,000 per person or £37 per person per week.)

In medicine, the gold standard is a double-blind randomised controlled trial, where researchers hold every factor constant across the populations in their study, except the thing they are testing (e.g. a drug or other intervention). The more people involved in the study, the better the statistics and the more meaningful (i.e. true) the results are likely to be. Remember that correlation is not causation. If a study shows that children who grow up playing more video games end up with lower IQs as adolescents, look for an explanation of the biological mechanism for what could be going on. If there isn’t one, tread cautiously. Seeing risks put into context is particularly important. If drinking coffee seemingly increases your risk of developing stomach cancer by 50 per cent, this is only worrying if the absolute risk of that cancer is itself significant.


The Autistic Brain: Thinking Across the Spectrum by Temple Grandin, Richard Panek

Asperger Syndrome, correlation does not imply causation, dark matter, David Brooks, deliberate practice, double helix, ghettoisation, Gregor Mendel, if you see hoof prints, think horses—not zebras, impulse control, Khan Academy, Mark Zuckerberg, meta-analysis, mouse model, neurotypical, pattern recognition, phenotype, Richard Feynman, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Steve Jobs, The future is already here, theory of mind, twin studies

So women who are pregnant or are thinking about becoming pregnant and who take antidepressants should consult a doctor and weigh the risks and benefits. In any case, we have to be very careful about looking for cause-and-effect relationships between environmental factors and genetics. As every scientist knows, correlation does not imply causation. An observed correlation—two events happening around the same time—might just be coincidence. Let’s use the now infamous vaccination controversy as a way to look at the logical complexity of a causation-versus-coincidence argument. The story goes like this. Parents routinely have their children vaccinated around age eighteen months.


pages: 483 words: 134,377

The Tyranny of Experts: Economists, Dictators, and the Forgotten Rights of the Poor by William Easterly

"Robert Solow", air freight, Andrei Shleifer, battle of ideas, Bretton Woods, British Empire, business process, business process outsourcing, Carmen Reinhart, clean water, colonial rule, correlation does not imply causation, creative destruction, Daniel Kahneman / Amos Tversky, Deng Xiaoping, desegregation, discovery of the americas, Edward Glaeser, en.wikipedia.org, European colonialism, Francisco Pizarro, fundamental attribution error, germ theory of disease, greed is good, Gunnar Myrdal, income per capita, invisible hand, James Watt: steam engine, Jane Jacobs, John Snow's cholera map, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, M-Pesa, microcredit, Monroe Doctrine, oil shock, place-making, Ponzi scheme, risk/return, road to serfdom, Silicon Valley, Steve Jobs, tacit knowledge, The Death and Life of Great American Cities, The Wealth of Nations by Adam Smith, Thomas L Friedman, urban planning, urban renewal, Washington Consensus, WikiLeaks, World Values Survey, young professional

The authors of the survey found a lot of poor people who contradicted the common assumption that poor people don’t care about their rights and care only about their material needs. EVIDENCE AND DEBATE The patterns discussed here do not prove that autocracy and collectivist values cause poverty—correlation is not causation. It could be that people who get rich for some other reason desire more individualism and democracy and are able to get it. Some studies cited here use some formal statistical methods to argue that a history of autocracy causes collectivist values, and both autocracy and collectivist values in turn cause poverty, but most economists find the methods used not very convincing.

The slave trade’s disastrous effects help explain a result that Nathan Nunn had already found in his doctoral dissertation—that among today’s African nations, those where Europeans had seized the most slaves were poorer than nations that had largely escaped slavery. Benin today is one of the poorest African nations.13 EVIDENCE WITH A CAUSE But once again correlation is not causation. It is plausible that the correlation could also run in reverse: poverty caused enslavement. Poorer people are less able to defend themselves because they cannot afford as many weapons as richer people. Also pre-existing lack of trust could have caused more enslavement. People who were already less trusting and less trustworthy are more likely to help the slavers by betraying their neighbors.


pages: 313 words: 94,490

Made to Stick: Why Some Ideas Survive and Others Die by Chip Heath, Dan Heath

affirmative action, Alan Greenspan, availability heuristic, Barry Marshall: ulcers, correlation does not imply causation, desegregation, Helicobacter pylori, Jeff Hawkins, low cost airline, Menlo Park, PalmPilot, Pepto Bismol, Ronald Reagan, Rosa Parks, shareholder value, Silicon Valley, Stephen Hawking, telemarketer

When Marshall presented their findings at a professional conference, the scientists snickered. One of the researchers who heard one of his presentations commented that he “simply didn’t have the demeanor of a scientist.” To be fair to the skeptics, they had a reasonable argument: Marshall and Warren’s evidence was based on correlation, not causation. Almost all of the ulcer patients seemed to have H. pylori. Unfortunately, there were also people who had H. pylori but no ulcer. And, as for proving causation, the researchers couldn’t very well dose a bunch of innocent people with bacteria to see whether they sprouted ulcers. By 1984, Marshall’s patience had run out.


pages: 108 words: 27,451

Magic Internet Money: A Book About Bitcoin by Jesse Berger

Alan Greenspan, barriers to entry, bitcoin, blockchain, Bretton Woods, capital controls, carbon footprint, correlation does not imply causation, cryptocurrency, diversification, diversified portfolio, Ethereum, ethereum blockchain, fiat currency, Firefox, forward guidance, Fractional reserve banking, George Gilder, inflation targeting, invisible hand, Johann Wolfgang von Goethe, liquidity trap, litecoin, Marshall McLuhan, Metcalfe’s law, Money creation, money: store of value / unit of account / medium of exchange, moral hazard, Network effects, Nixon shock, Nixon triggered the end of the Bretton Woods system, oil shale / tar sands, price mechanism, Ralph Waldo Emerson, rent-seeking, reserve currency, ride hailing / ride sharing, risk tolerance, Robert Metcalfe, Satoshi Nakamoto, the medium is the message

According to the December 2019 Mining Update from CoinShares, a digital asset management firm, major mining operations are currently situated in regions with large, unused supplies of renewable energy because they tend to operate below peak capacity.32 This aligns with miners’ incentive to use energy efficiently, and also unlocks value from previously uneconomical renewable sources. Despite the report concluding that 73% of mining energy is derived from renewable sources, critics remind us that correlation does not imply causation.33 Some renewable energy sources can only provide energy on an intermittent basis, but mining tends to require a constant flow. As a result, it is possible that some miners may have a partial need for “dirty” energy, despite any best laid plans. 11.6.3 Mining: Of Central Concern “If you know the enemy and know yourself, you need not fear the result of a hundred battles.”


Beautiful Data: The Stories Behind Elegant Data Solutions by Toby Segaran, Jeff Hammerbacher

23andMe, airport security, Amazon Mechanical Turk, bioinformatics, Black Swan, business intelligence, card file, cloud computing, computer vision, correlation coefficient, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, data acquisition, data science, database schema, double helix, en.wikipedia.org, epigenetics, fault tolerance, Firefox, Gregor Mendel, Hans Rosling, housing crisis, information retrieval, lake wobegon effect, longitudinal study, Mars Rover, natural language processing, openstreetmap, prediction markets, profit motive, semantic web, sentiment analysis, Simon Singh, social graph, SPARQL, speech recognition, statistical model, supply-chain management, text mining, Vernor Vinge, web application

The research described never suggested a causal link, but the journalist offered her own advice to couples based on the “data.” The substitution of correlation with causality need not be so explicit. When a scientific research project is undertaken, there exists the assumption that correlation, if discovered, would imply causation, albeit unknown. Else, why seek to answer a research question at all: large-scale search for correlation without causation is aleatory computation, not science. Even with so-called big data, science remains an intensely hypothesis-driven process. The limits of empirical research is not grounds to throw up our hands, only to be careful to push discovery forward without getting rosy-eyed about causality.


pages: 281 words: 95,852

The Googlization of Everything: by Siva Vaidhyanathan

1960s counterculture, activist fund / activist shareholder / activist investor, AltaVista, barriers to entry, Berlin Wall, borderless world, Burning Man, Cass Sunstein, choice architecture, cloud computing, computer age, corporate social responsibility, correlation does not imply causation, creative destruction, data acquisition, death of newspapers, don't be evil, Firefox, Francis Fukuyama: the end of history, full text search, global pandemic, global village, Google Earth, Howard Rheingold, Ian Bogost, independent contractor, informal economy, information retrieval, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge worker, libertarian paternalism, market fundamentalism, Marshall McLuhan, means of production, Mikhail Gorbachev, moral panic, Naomi Klein, Network effects, new economy, Nicholas Carr, PageRank, Panopticon Jeremy Bentham, pirate software, Ray Kurzweil, Richard Thaler, Ronald Reagan, side project, Silicon Valley, Silicon Valley ideology, single-payer health, Skype, Social Responsibility of Business Is to Increase Its Profits, social web, Steven Levy, Stewart Brand, technoutopianism, The Nature of the Firm, The Structural Transformation of the Public Sphere, Thorstein Veblen, Tyler Cowen, urban decay, web application, Yochai Benkler, zero-sum game

But here Anderson has stepped out even beyond the pop sociology and economics that usually dominate the magazine. Anderson claims “correlation is enough.”41 In other words, the entire process of generating scientific (or, for that matter, social-scientific) theories and modestly limiting claims to correlation without causation is obsolete and quaint: given enough data and enough computing power, you can draw strong enough correlations to claim with confidence that what you have discovered is indisputably true. THE GOOGL I ZAT I ON OF ME MORY 197 The risk here is more than one of intellectual hubris: the academy has no dearth of that.


pages: 571 words: 105,054

Advances in Financial Machine Learning by Marcos Lopez de Prado

algorithmic trading, Amazon Web Services, asset allocation, backtesting, bioinformatics, Brownian motion, business process, Claude Shannon: information theory, cloud computing, complexity theory, correlation coefficient, correlation does not imply causation, data science, diversification, diversified portfolio, en.wikipedia.org, financial engineering, fixed income, Flash crash, G4S, implied volatility, information asymmetry, latency arbitrage, margin call, market fragmentation, market microstructure, martingale, NP-complete, P = NP, p-value, paper trading, pattern recognition, performance metric, profit maximization, quantitative trading / quantitative finance, RAND corporation, random walk, risk free rate, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, Silicon Valley, smart cities, smart meter, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, traveling salesman

The cost of lending and the amount available is generally unknown, and depends on relations, inventory, relative demand, etc. These are just a few basic errors that most papers published in journals make routinely. Other common errors include computing performance using a non-standard method (Chapter 14); ignoring hidden risks; focusing only on returns while ignoring other metrics; confusing correlation with causation; selecting an unrepresentative time period; failing to expect the unexpected; ignoring the existence of stop-out limits or margin calls; ignoring funding costs; and forgetting practical aspects (Sarfati [2015]). There are many more, but really, there is no point in listing them, because of the title of the next section. 11.3 Even If Your Backtest Is Flawless, It Is Probably Wrong Congratulations!


Data and the City by Rob Kitchin,Tracey P. Lauriault,Gavin McArdle

A Declaration of the Independence of Cyberspace, algorithmic management, bike sharing scheme, bitcoin, blockchain, Bretton Woods, Chelsea Manning, citizen journalism, Claude Shannon: information theory, clean water, cloud computing, complexity theory, conceptual framework, corporate governance, correlation does not imply causation, create, read, update, delete, crowdsourcing, cryptocurrency, data science, dematerialisation, digital map, distributed ledger, fault tolerance, fiat currency, Filter Bubble, floating exchange rates, functional programming, global value chain, Google Earth, Hacker News, hive mind, information security, Internet of things, Kickstarter, knowledge economy, lifelogging, linked data, loose coupling, new economy, New Urbanism, Nicholas Carr, open economy, openstreetmap, OSI model, packet switching, pattern recognition, performance metric, place-making, RAND corporation, RFID, Richard Florida, ride hailing / ride sharing, semantic web, sentiment analysis, sharing economy, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, smart grid, smart meter, social graph, software studies, statistical model, tacit knowledge, TaskRabbit, text mining, The Chicago School, The Death and Life of Great American Cities, the market place, the medium is the message, the scientific method, Toyota Production System, urban planning, urban sprawl, web application

Rob Kitchin (2014a) has described how a new empiricist school of thought has emerged that takes the data these computer systems are generating at face value to produce direct insights in (amongst others) urban patterns. As one of their protagonists, Chris Anderson (2008), claims: We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot . . . Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all. In their vision, Batty’s observation about the use of computers in the city has come full circle: computer systems produce data about the city that allegedly give us a transparent look into the city’s dynamics.


pages: 181 words: 52,147

The Driver in the Driverless Car: How Our Technology Choices Will Create the Future by Vivek Wadhwa, Alex Salkever

23andMe, 3D printing, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, Bernie Sanders, bitcoin, blockchain, clean water, correlation does not imply causation, distributed ledger, Donald Trump, double helix, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Google bus, Hyperloop, income inequality, information security, Internet of things, job automation, Kevin Kelly, Khan Academy, Kickstarter, Law of Accelerating Returns, license plate recognition, life extension, longitudinal study, Lyft, M-Pesa, Menlo Park, microbiome, military-industrial complex, mobile money, new economy, personalized medicine, phenotype, precision agriculture, radical life extension, RAND corporation, Ray Kurzweil, recommendation engine, Ronald Reagan, Second Machine Age, self-driving car, Silicon Valley, Skype, smart grid, stem cell, Stephen Hawking, Steve Wozniak, Stuxnet, supercomputer in your pocket, Tesla Model S, The future is already here, The Future of Employment, Thomas Davenport, Travis Kalanick, Turing test, Uber and Lyft, Uber for X, uber lyft, uranium enrichment, Watson beat the top human players on Jeopardy!, zero day

In February 2015, researchers from M.I.T. and from Harvard University released the results of the most comprehensive longitudinal study yet of how the diversity and types of gut flora affect onset of this type of diabetes.3 The scientists tracked what happened to the gut bacteria of a large number of subjects from birth to their third year in life, and found that children who became diabetic suffered a 25 percent reduction in their gut bacteria’s diversity. What’s more, the mix of bacteria shifted away from types known to promote health toward types known to promote inflammation. Correlation is not causation, but the results added to evidence that the bacteria in our intestines have a strong effect on our health. In fact, manipulating the microbiome may even become more important than genomics and gene-based medicine. Unlike genomics and gene therapy, which require a relatively heroic effort to induce physiological changes, tweaking the microbiome appears to be relatively straightforward and safe: just mix up a cocktail of the appropriate bacteria, and transplant it into your gut.


pages: 372 words: 111,573

10% Human: How Your Body's Microbes Hold the Key to Health and Happiness by Alanna Collen

Asperger Syndrome, Barry Marshall: ulcers, Berlin Wall, biofilm, clean water, correlation does not imply causation, David Strachan, discovery of penicillin, Drosophila, Edward Jenner, Fall of the Berlin Wall, friendly fire, germ theory of disease, global pandemic, Helicobacter pylori, hygiene hypothesis, Ignaz Semmelweis: hand washing, illegal immigration, John Snow's cholera map, Kickstarter, Louis Pasteur, Maui Hawaii, meta-analysis, microbiome, phenotype, placebo effect, the scientific method

It turned out that those who had been given antibiotics before the age of two – a startling 74 per cent of them – were on average nearly twice as likely to have developed asthma by the time they were eight. The more courses of antibiotics the children received, the more likely they were to develop asthma, eczema and hay fever. But, as the saying goes, correlation does not always mean causation. The lead researcher on the antibiotics study had discovered four years earlier that the more television children watched, the more likely they were to develop asthma. Of course, despite similar results as in the antibiotics study, no one really believed that the act of watching television could bring about immune dysfunction in the lungs.

It also takes time for the effects to become clear across populations, countries and continents. If the introduction of antibiotics in 1944 is in some way responsible for our current state of health, the 1950s are exactly when we would expect to see the dawning of their impact. Let us not jump the gun though. As any scientist would hasten to point out, correlation does not always mean causation. The timely introduction of antibiotics may be as unrealistic a connection to rising chronic illness as the self-serve supermarkets that made their debut in the 1940s. Connections alone, whilst useful guides, do not always provide a causal link. An amusing website about spurious correlations tells me that there’s an impressively close correlation between per capita consumption of cheese in the US and the number of people who die each year by becoming tangled in their bed sheets.


pages: 321 words: 85,893

The Vegetarian Myth: Food, Justice, and Sustainability by Lierre Keith

British Empire, car-free, clean water, cognitive dissonance, correlation does not imply causation, Drosophila, dumpster diving, en.wikipedia.org, Gary Taubes, Haber-Bosch Process, longitudinal study, McMansion, meta-analysis, military-industrial complex, out of africa, peak oil, placebo effect, Rosa Parks, the built environment

The kind of cross-country comparison that Keys did “involves comparing apples with oranges—that is countries with widely varying cultural, social, political and physical environments.”52 With such an infinite number of variables, a finding of definitive causation would be ridiculous. Figure 4A. Correlation between the total fat consumption as a percent of total calorie consumption, and mortality from coronary heart disease in six countries. Redrawn from The Cholesterol Myths by Uffe Ravnskov. John Yudkin’s 1957 study shows the error of conflating correlation with causation. You can see from Figure 4B (page over) that owning a TV and radio had a much stronger association with Coronary Heart Disease (CHD) than any nutritional elements.53 But no one would suggest that TV causes CHD, or that sacrificing our TVs will grant us a longer life. No one went on to investigate whether TVs produced heart-stopping emissions or blood-damaging toxins.


pages: 322 words: 107,576

Bad Science by Ben Goldacre

Asperger Syndrome, correlation does not imply causation, disinformation, Edward Jenner, experimental subject, hygiene hypothesis, Ignaz Semmelweis: hand washing, John Snow's cholera map, Louis Pasteur, meta-analysis, Nelson Mandela, offshore financial centre, p-value, placebo effect, publication bias, Richard Feynman, risk tolerance, Ronald Reagan, selection bias, selective serotonin reuptake inhibitor (SSRI), the scientific method, urban planning

I understand if you want to skip it, but know that it is here for the doctors who bought the book to laugh at homeopaths. Here are the classic tricks to play in your statistical analysis to make sure your trial has a positive result. Ignore the protocol entirely Always assume that any correlation proves causation. Throw all your data into a spreadsheet programme and report—as significant—any relationship between anything and everything if it helps your case. If you measure enough, some things are bound to be positive just by sheer luck. Play with the baseline Sometimes, when you start a trial, quite by chance the treatment group is already doing better than the placebo group.


pages: 465 words: 109,653

Free Ride by Robert Levine

A Declaration of the Independence of Cyberspace, Anne Wojcicki, book scanning, borderless world, Buckminster Fuller, citizen journalism, commoditize, correlation does not imply causation, creative destruction, crowdsourcing, death of newspapers, Edward Lloyd's coffeehouse, Electric Kool-Aid Acid Test, Firefox, future of journalism, Googley, Hacker Ethic, informal economy, Jaron Lanier, John Perry Barlow, Joi Ito, Julian Assange, Justin.tv, Kevin Kelly, linear programming, Marc Andreessen, Mitch Kapor, moral panic, offshore financial centre, pets.com, publish or perish, race to the bottom, Saturday Night Live, Silicon Valley, Silicon Valley startup, Skype, spectrum auction, Steve Jobs, Steven Levy, Stewart Brand, subscription business, Telecommunications Act of 1996, Whole Earth Catalog, WikiLeaks

Many factors have hurt music sales, including the closing of so many record stores. But almost every other study has concluded that file sharing played a role,74 and anyone who believes otherwise is running out of alternate explanations. Several studies have shown that individuals who download music illegally also buy it, but that proves only correlation, not causation. Some suggested CD sales fell because music fans are no longer replacing their old records, but “catalog” sales of older releases declined less than overall sales from 2004 to 2009.75 Others speculated that DVD sales cut into the CD market, but now they’re declining as well. Music sales have also declined disproportionately in countries where file sharing is more common.


pages: 396 words: 117,149

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, backpropagation, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Charles Babbage, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is the new oil, data science, double helix, Douglas Hofstadter, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, global village, Google Glasses, Gödel, Escher, Bach, Hans Moravec, information retrieval, Jeff Hawkins, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, lone genius, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, scientific worldview, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, Stanford marshmallow experiment, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight, zero-sum game

It can also be generalized to try many combinations of changes at once, without losing track of which changes lead to which gains (or losses). Companies like Amazon and Google swear by it; you’ve probably participated in thousands of A/B tests without realizing it. A/B testing gives the lie to the oft-heard criticism that big data is only good for finding correlations, not causation. Philosophical fine points aside, learning causality is learning the effects of your actions, and anyone with a stream of data they can affect can do it—from a one-year-old splashing around in the bathtub to a president campaigning for reelection. Learning to relate If we endow Robby the robot with all the learning abilities we’ve seen so far in this book, he’ll be pretty smart but still a bit autistic.


pages: 425 words: 112,220

The Messy Middle: Finding Your Way Through the Hardest and Most Crucial Part of Any Bold Venture by Scott Belsky

23andMe, 3D printing, Airbnb, Albert Einstein, Anne Wojcicki, augmented reality, autonomous vehicles, Ben Horowitz, bitcoin, blockchain, Chuck Templeton: OpenTable:, commoditize, correlation does not imply causation, cryptocurrency, data science, delayed gratification, DevOps, Donald Trump, Elon Musk, endowment effect, hiring and firing, Inbox Zero, iterative process, Jeff Bezos, knowledge worker, Lean Startup, Lyft, Mark Zuckerberg, Marshall McLuhan, minimum viable product, move fast and break things, move fast and break things, NetJets, Network effects, new economy, old-boy network, pattern recognition, Paul Graham, ride hailing / ride sharing, Salesforce, Sheryl Sandberg, Silicon Valley, slashdot, Snapchat, Steve Jobs, subscription business, TaskRabbit, the medium is the message, Travis Kalanick, Uber for X, uber lyft, WeWork, Y Combinator, young professional

This gives your staff ownership over their work and also allows the most knowledgeable person to lead the discussion. False attribution can wreak havoc in a team. This applies just as much to congratulating the wrong person as it does putting a success down to circumstance instead of skill. In our effort to optimize whatever seems to work, we’re liable to conflate correlation with causation. If things go well, it doesn’t necessarily mean someone’s tactics worked. Go a level deeper to understand whether a success resulted from good timing, external market forces, great skills and execution, or some combination of the above. Attribute success at the element level: the skills, decisions, tactics, relationships, and hard work that contributed to the outcome.


pages: 514 words: 111,012

The Art of Monitoring by James Turnbull

Amazon Web Services, anti-pattern, cloud computing, continuous integration, correlation does not imply causation, Debian, DevOps, domain-specific language, failed state, functional programming, Kickstarter, Kubernetes, microservices, performance metric, pull request, Ruby on Rails, software as a service, source of truth, web application, WebSocket

Throughout the book we'll look at ways to visualize the data and metrics we've collected. However metrics and their visualizations are often tricky to interpret. Humans tend towards apophenia—the perception of meaningful patterns within random data—when viewing visualizations. This often leads to sudden leaps from correlation to causation. This can be further exacerbated by the granularity and resolution of our available data, how we choose to represent it, and the scale on which we represent it. Our ideal visualizations will clearly show the data, with an emphasis on highlighting substance over visuals. In this book we've tried to build visuals that subscribe to these broad rules: Clearly show the data.


pages: 349 words: 114,038

Culture & Empire: Digital Revolution by Pieter Hintjens

4chan, Aaron Swartz, airport security, AltaVista, anti-communist, anti-pattern, barriers to entry, Bill Duvall, bitcoin, blockchain, Boeing 747, business climate, business intelligence, business process, Chelsea Manning, clean water, commoditize, congestion charging, Corn Laws, correlation does not imply causation, cryptocurrency, Debian, disinformation, Edward Snowden, failed state, financial independence, Firefox, full text search, gamification, German hyperinflation, global village, GnuPG, Google Chrome, greed is good, Hernando de Soto, hiring and firing, independent contractor, informal economy, intangible asset, invisible hand, James Watt: steam engine, Jeff Rulifson, Julian Assange, Kickstarter, M-Pesa, mass immigration, mass incarceration, mega-rich, military-industrial complex, MITM: man-in-the-middle, mutually assured destruction, Naomi Klein, national security letter, Nelson Mandela, new economy, New Urbanism, Occupy movement, offshore financial centre, packet switching, patent troll, peak oil, pre–internet, private military company, race to the bottom, rent-seeking, reserve currency, RFC: Request For Comment, Richard Feynman, Richard Stallman, Ross Ulbricht, Satoshi Nakamoto, security theater, selection bias, Skype, slashdot, software patent, spectrum auction, Steve Crocker, Steve Jobs, Steven Pinker, Stuxnet, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, trade route, transaction costs, twin studies, union organizing, wealth creators, web application, WikiLeaks, Y2K, zero day, Zipf's Law

Conflict is always political, yet leaders often invoke religion to bolster their followers, and create more tribalism. Outsiders, searching for simplistic explanations, and possibly arms sales, embrace this rhetoric as reality. As the conflict increases, the religious arguments will definitely increase. However, it's correlation, not causation. And in the end, the solution comes from addressing the original political issues. Until then, and as long as possible, the beneficiaries (war can be incredibly profitable!) will pump up the "irreconcilable ancient hatreds" angle. And so it goes with the Global Extremist Islamic Threat to Modern Civilization.


pages: 384 words: 112,971

What’s Your Type? by Merve Emre

Albert Einstein, anti-communist, card file, correlation does not imply causation, emotional labour, Frederick Winslow Taylor, God and Mammon, Golden Gate Park, hiring and firing, Ida Tarbell, index card, Isaac Newton, job satisfaction, late capitalism, means of production, Menlo Park, mutually assured destruction, Norman Mailer, p-value, Panopticon Jeremy Bentham, Ralph Waldo Emerson, scientific management, Socratic dialogue, Stanford prison experiment, traveling salesman, upwardly mobile, uranium enrichment, women in the workforce

The RCA 501 made it possible to score tests at an inhuman rate—one test every nine seconds in 1958, the year the computer first arrived at ETS. It also allowed ETS’s statisticians to write programs that could perform basic statistical analyses across large samples of test subjects: the calculation of means and modes, correlations and causations, the determination of significance. But the computer was more than just a convenient time- and labor-saving device. It was an indispensable technology given the new scale of Isabel’s and ETS’s operations. In just the first few years of their partnership, the indicator swept the country from east to west and back again with a frenetic, vital energy.


Designing the Mind: The Principles of Psychitecture by Designing the Mind, Ryan A Bush

Albert Einstein, algorithmic bias, augmented reality, butterfly effect, carbon footprint, cognitive bias, correlation does not imply causation, data science, delayed gratification, deliberate practice, effective altruism, Elon Musk, en.wikipedia.org, endowment effect, fundamental attribution error, hedonic treadmill, hindsight bias, impulse control, Kevin Kelly, Lao Tzu, lifelogging, longitudinal study, loss aversion, meta-analysis, Own Your Own Home, pattern recognition, price anchoring, randomized controlled trial, Silicon Valley, Stanford marshmallow experiment, Steven Pinker, Walter Mischel

A slippery slope argument claims that a small step will inevitably lead to a whole chain of undesirable consequences, such as parents arguing that if they let their daughter learn a card trick, there will be no stopping her from pursuing a career as an illusionist. A false dichotomy claims that if one extreme is rejected (capitalism has no flaws), another extreme must be the only alternative (communism it is). And the post hoc fallacy causes us to assume that correlation equates to causation, such as the belief that the sun rising actually causes your drinking problem. Once you have familiarized yourself with the full list of common fallacies, they start appearing everywhere. It is hard to imagine what political debates would look like if candidates knew they would be called out for every red herring or faulty generalization committed.


pages: 163 words: 42,402

pages: 407 words: 108,030

How to Talk to a Science Denier: Conversations With Flat Earthers, Climate Deniers, and Others Who Defy Reason by Lee McIntyre

2021 United States Capitol attack, Affordable Care Act / Obamacare, Alfred Russel Wallace, An Inconvenient Truth, Boris Johnson, Climategate, cognitive bias, cognitive dissonance, coronavirus, correlation does not imply causation, COVID-19, different worldview, disinformation, Donald Trump, en.wikipedia.org, Eratosthenes, experimental subject, Intergovernmental Panel on Climate Change (IPCC), Mark Zuckerberg, obamacare, Paris climate accords, precautionary principle, Recombinant DNA, Richard Feynman, scientific mainstream, selection bias, sovereign wealth fund, stem cell, Steven Levy, the scientific method, University of East Anglia, Upton Sinclair, Virgin Galactic, WikiLeaks

But the problem is that for such concerns to be scientifically valid—and thus count as skepticism rather than denialism—they must be backed up by evidence. And, for GMOs, where is it? With vaccines, there is the Vaccine Adverse Events Reporting System (VAERS), which documents and catalogs the vanishingly small number of “adverse” events so that they can be investigated. But as statisticians know, correlation does not necessarily indicate causation. Just because a child had an adverse reaction near the time they had a vaccine, this does not mean the vaccine caused it. This is why scientists who have access to the VAERS system must investigate. Then they must decide what to do in those rare instances when people do have an adverse reaction that can be traced to a vaccine.


pages: 538 words: 121,670

Republic, Lost: How Money Corrupts Congress--And a Plan to Stop It by Lawrence Lessig

air traffic controllers' union, Alan Greenspan, asset-backed security, banking crisis, carried interest, circulation of elites, cognitive dissonance, corporate personhood, correlation does not imply causation, crony capitalism, David Brooks, Edward Glaeser, Filter Bubble, financial deregulation, financial innovation, financial intermediation, Greenspan put, invisible hand, jimmy wales, Martin Wolf, meta-analysis, Mikhail Gorbachev, moral hazard, Pareto efficiency, place-making, profit maximization, Ralph Nader, regulatory arbitrage, rent-seeking, Ronald Reagan, Sam Peltzman, Savings and loan crisis, Silicon Valley, single-payer health, The Wealth of Nations by Adam Smith, too big to fail, Tyler Cowen, upwardly mobile, WikiLeaks, Yochai Benkler, Zipcar

As Fiorina and Abrams put it, “the natural place to look for campaign money is in the ranks of the single-issue groups, and a natural strategy to motivate their members is to exaggerate the threats their enemies pose.”29 In this odd and certainly unintended way, then, the demand for cash could also be changing the substance of American politics. Could be, because all I’ve described is correlation, not causation. But at a minimum the correlation should concern us: On some issues, the parties become more united—those issues that appeal to corporate America. On other issues, the parties become more divided—the more campaign funds an issue inspires, the more extremely it gets framed. In both cases, the change correlates with a strategy designed to maximize campaign cash, while weakening the connection between what Congress does (or at least campaigns on) and the potential needs of ordinary Americans.


pages: 473 words: 121,895

Come as You Are: The Surprising New Science That Will Transform Your Sex Life by Emily Nagoski Ph.d.

cognitive dissonance, correlation does not imply causation, delayed gratification, meta-analysis, placebo effect, Skype, Snapchat, spaced repetition, the scientific method, twin studies

Suppose you recognize that nonconcordance exists, you acknowledge that it’s expecting without necessarily indicating enjoying or eagerness, and then you read the research that shows there is a correlation between nonconcordance and sexual dysfunctions related to desire and arousal.21 And so you decide that, because nonconcordance is associated with dysfunction, nonconcordance must be a problem. Which brings me to a sentence every undergraduate who takes a research methods class will memorize: “Correlation does not imply causation.” It refers to the cum hoc ergo propter hoc fallacy—“with this, therefore because of this”—which means that just because two things happen together doesn’t mean that one thing caused the other thing. The quintessential example in the twenty-first century is the relationship between pirates and global warming.22 This is a joke made by Bobby Henderson, as part of the belief system of the Church of the Flying Spaghetti Monster.


pages: 252 words: 74,167

Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future by Luke Dormehl

Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic management, algorithmic trading, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, book scanning, borderless world, call centre, cellular automata, Charles Babbage, Claude Shannon: information theory, cloud computing, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, drone strike, Elon Musk, Flash crash, friendly AI, game design, global village, Google X / Alphabet X, Hans Moravec, hive mind, industrial robot, information retrieval, Internet of things, iterative process, Jaron Lanier, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, life extension, Loebner Prize, Marc Andreessen, Mark Zuckerberg, Menlo Park, natural language processing, Norbert Wiener, out of africa, PageRank, pattern recognition, radical life extension, Ray Kurzweil, recommendation engine, remote working, RFID, scientific management, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, social intelligence, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, tech billionaire, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, too big to fail, Turing machine, Turing test, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!

The case works by measuring the heart’s electrical patterns through the fingertips of the person holding it. An algorithm then analyses the regularity of their heartbeat and suggests if the person should see a doctor. As our environment gets ever smarter, we will enter an age of continuous, real-time risk assessments. For the first time in history it will be possible to draw constant correlations, and possibly causations, between a large number of genomic, physiological, biological and environmental factors on an individual basis. Wearable devices will tirelessly monitor our heart rate, blood oxygen levels, physical activity, breathing patterns, facial expression, lung function, voice inflection, brain waves, posture, sleep quality and more, in addition to external measurements like air quality and noise level.


pages: 239 words: 77,436

Pure, White and Deadly: How Sugar Is Killing Us and What We Can Do to Stop It by John Yudkin

correlation coefficient, correlation does not imply causation, discovery of penicillin, disinformation

But to learn that Yudkin foresaw what a problem sugar was thirty-six years earlier, and at a much lower dose (i.e. before the advent of high-fructose corn syrup and the two-litre bottle) was a true revelation. Indeed, I was a Yudkin disciple and I hadn’t even realized it. Yudkin didn’t have the voluminous data that exist today. He had correlation, but not causation. He didn’t have mechanism. He didn’t know that sugar caused insulin resistance by being turned into fat in the liver through the process of de novo lipogenesis, or that sugar induced protein damage through the Maillard or browning reaction. He didn’t know that sugar was weakly addictive, although he surmised it.


pages: 268 words: 74,724

Who Needs the Fed?: What Taylor Swift, Uber, and Robots Tell Us About Money, Credit, and Why We Should Abolish America's Central Bank by John Tamny

Airbnb, Alan Greenspan, bank run, Bear Stearns, Bernie Madoff, bitcoin, Bretton Woods, buy and hold, Carmen Reinhart, corporate raider, correlation does not imply causation, cotton gin, creative destruction, Credit Default Swap, crony capitalism, crowdsourcing, Donald Trump, Downton Abbey, Fairchild Semiconductor, fiat currency, financial innovation, Fractional reserve banking, full employment, George Gilder, Home mortgage interest deduction, Jeff Bezos, job automation, Joseph Schumpeter, junk bonds, Kenneth Rogoff, Kickstarter, liquidity trap, Mark Zuckerberg, market bubble, Michael Milken, Money creation, money market fund, moral hazard, mortgage tax deduction, NetJets, offshore financial centre, oil shock, peak oil, Peter Thiel, Phillips curve, price stability, profit motive, quantitative easing, race to the bottom, Ronald Reagan, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Steve Jobs, The Wealth of Nations by Adam Smith, too big to fail, Travis Kalanick, Uber for X, War on Poverty, yield curve

But for the purposes of this chapter, FDR’s dollar meddling requires discussion, because one of the most common objections to the Federal Reserve is that since its creation in 1913, the dollar has lost more than 90 percent of its value. It’s a horrid number, and the unseen is the massive economic advances that would have made the abundant present seem impoverished by comparison but that did not come into being. However, this objection to the Fed is one of those instances where correlation is not causation. Lest we forget, FDR decided to devalue the dollar, and per Shlaes, “It did not matter what the Federal Reserve said.” Stated simply, the first major decline in the value of the dollar had nothing to do with the Fed. So incensed was Fed Chairman Eugene Meyer by FDR’s decision that he actually resigned.6 Let’s shift to 1944 and the Bretton Woods monetary conference at the Mount Washington Hotel.


pages: 246 words: 74,404

Do Nothing: How to Break Away From Overworking, Overdoing, and Underliving by Celeste Headlee

8-hour work day, agricultural Revolution, airport security, Atul Gawande, Bertrand Russell: In Praise of Idleness, correlation does not imply causation, deliberate practice, Downton Abbey, Elon Musk, estate planning, financial independence, Ford paid five dollars a day, gamification, hedonic treadmill, helicopter parent, Henri Poincaré, hive mind, income inequality, James Watt: steam engine, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, knowledge worker, Lyft, new economy, Parkinson's law, performance metric, Ronald Reagan, Silicon Valley, Snapchat, Steve Jobs, tech billionaire, tech worker, The Theory of the Leisure Class by Thorstein Veblen, The Wisdom of Crowds, theory of mind, Thorstein Veblen, Torches of Freedom, trickle-down economics, uber lyft, women in the workforce

This self-inflicted pressure has a very high cost on the human mind and body. Unreasonably high standards and severe self-criticism are linked to high blood pressure, depression, eating disorders, and suicidal ideation. Therapists will tell you that you cannot both strive to be perfect and enjoy good mental health. They are mutually exclusive. While correlation is not causation, it’s important to note that suicides among young people have risen by 56 percent since 1999. One Pennsylvania teacher connects the rise in suicides to the increase in standardized testing. “American students are increasingly being sorted and evaluated by reference to their test score rather than their classroom grade or other academic indicators,” Steven Singer wrote on HuffPost.


pages: 428 words: 134,832

Straphanger by Taras Grescoe

active transport: walking or cycling, Affordable Care Act / Obamacare, airport security, Albert Einstein, big-box store, bike sharing scheme, Boeing 747, Boris Johnson, British Empire, call centre, car-free, carbon footprint, City Beautiful movement, congestion charging, correlation does not imply causation, David Brooks, deindustrialization, East Village, edge city, Enrique Peñalosa, extreme commuting, financial deregulation, Frank Gehry, glass ceiling, Golden Gate Park, high-speed rail, housing crisis, hydraulic fracturing, indoor plumbing, intermodal, invisible hand, Jane Jacobs, jitney, Joan Didion, Kickstarter, Kitchen Debate, laissez-faire capitalism, Marshall McLuhan, mass immigration, McMansion, megacity, megaproject, mortgage tax deduction, Network effects, New Urbanism, obamacare, oil shale / tar sands, oil shock, Own Your Own Home, peak oil, pension reform, Peter Calthorpe, Ponzi scheme, Ronald Reagan, Rosa Parks, sensible shoes, Silicon Valley, Skype, the built environment, The Death and Life of Great American Cities, the High Line, transit-oriented development, union organizing, urban planning, urban renewal, urban sprawl, walkable city, white flight, working poor, young professional, Zipcar

One thing the nation’s worst crime hot spots seem to have in common is that they are highly sprawled metropolitan regions—Greater St. Louis covers almost 8,500 square miles—whose atrophied public transport systems make their residents almost completely dependent on cars. Any responsible criminologist would protest that only a fool confounds correlation and causation. Fair enough, though this raises another question: Doesn’t believing that your transportation and housing choices shield you from crime when they actually make you more likely to be a victim of it mean you are already living in a fool’s paradise? Rubber and Rail Planners are at ease talking about residential densities, workplace clusters, and transit ridership rates, but they are strangely silent on the role skin color plays in public transport.


pages: 492 words: 141,544

Red Moon by Kim Stanley Robinson

artificial general intelligence, basic income, blockchain, Brownian motion, correlation does not imply causation, cryptocurrency, Deng Xiaoping, gig economy, Hyperloop, illegal immigration, income inequality, invisible hand, Ken Thompson, Kim Stanley Robinson, low earth orbit, Magellanic Cloud, megacity, precariat, Schrödinger's Cat, seigniorage, strong AI, Turing machine, universal basic income, zero-sum game

An amateur astronomer observing the moon was in the beam’s target circle, and captured a recording of part of it. It was an encrypted message.” “And you broke the code?” “No. But the timing of this message is suggestive. An hour after this light from the moon was seen, people from all over China began to head for Beijing.” “Coincidence?” Zhou suggested. “Correlation, not causation?” Bo and Dhu did not reply. Ta Shu saw that Zhou was not going to share anything with these two, just out of a general sense of caution. War of the agencies at least, and maybe something more. The discipline inspection commission didn’t have much direct presence on the moon, so far as Ta Shu knew, even if they did oversee the Lunar Authority as they did all the agencies.


pages: 470 words: 130,269

The Marginal Revolutionaries: How Austrian Economists Fought the War of Ideas by Janek Wasserman

Albert Einstein, American Legislative Exchange Council, anti-communist, battle of ideas, Berlin Wall, Bretton Woods, business cycle, collective bargaining, Corn Laws, correlation does not imply causation, creative destruction, David Ricardo: comparative advantage, different worldview, Donald Trump, experimental economics, Fall of the Berlin Wall, floating exchange rates, Fractional reserve banking, Francis Fukuyama: the end of history, full employment, Gunnar Myrdal, housing crisis, Internet Archive, invisible hand, John von Neumann, Joseph Schumpeter, laissez-faire capitalism, liberal capitalism, market fundamentalism, mass immigration, means of production, Menlo Park, military-industrial complex, Mont Pelerin Society, New Journalism, New Urbanism, old-boy network, Paul Samuelson, Philip Mirowski, price mechanism, price stability, RAND corporation, random walk, rent control, road to serfdom, Robert Bork, rolodex, Ronald Coase, Ronald Reagan, Silicon Valley, Simon Kuznets, The Chicago School, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, trade liberalization, union organizing, urban planning, Vilfredo Pareto, Washington Consensus, zero-sum game, éminence grise

Simmering beneath the surface for over a decade, petty resentments erupted into an academic contretemps in 1883. The vitriol bewildered participants and observers. The debate resolved little and cost much. More than any other event, the dispute precipitated the formation of a distinctive Austrian School, yet one must not confuse correlation and causation: the latter-day Austrian approach owed little to this contest of egos. Instead, a new cohort of scholars entered the field simultaneously with the struggle and began to enrich the embryonic Austrian approach.32 In the decade after Principles appeared, it barely made an impression outside of Vienna.


pages: 504 words: 129,087

The Ones We've Been Waiting For: How a New Generation of Leaders Will Transform America by Charlotte Alter

"side hustle", 4chan, affirmative action, Affordable Care Act / Obamacare, basic income, Berlin Wall, Bernie Sanders, Big Tech, carbon footprint, clean water, collective bargaining, Columbine, corporate personhood, correlation does not imply causation, Credit Default Swap, crowdsourcing, data science, David Brooks, disinformation, Donald Trump, double helix, East Village, ending welfare as we know it, Fall of the Berlin Wall, feminist movement, Ferguson, Missouri, financial deregulation, Francis Fukuyama: the end of history, gig economy, glass ceiling, Google Hangouts, Greta Thunberg, housing crisis, illegal immigration, immigration reform, income inequality, Intergovernmental Panel on Climate Change (IPCC), job-hopping, Kevin Kelly, knowledge economy, Lyft, mandatory minimum, Marc Andreessen, Mark Zuckerberg, mass incarceration, McMansion, medical bankruptcy, microaggression, move fast and break things, move fast and break things, Nate Silver, obamacare, Occupy movement, opioid epidemic / opioid crisis, passive income, pre–internet, race to the bottom, RAND corporation, Ronald Reagan, sexual politics, Sheryl Sandberg, Silicon Valley, single-payer health, Snapchat, Steve Bannon, TaskRabbit, tech bro, too big to fail, Uber and Lyft, uber lyft, universal basic income, unpaid internship, We are the 99%, white picket fence, working poor, Works Progress Administration

It’s hard to establish whether millennials were influenced by Harry Potter or if the series took off because it touched on themes that were already brewing in young minds in the late 1990s and early 2000s. Most likely, the huge influence of Harry Potter and the rise of progressive attitudes among millennials are correlated, not causational. Harry’s world—the heroes and villains, the assumptions and challenges—largely mirrors millennial attitudes about what’s good (teamwork, diversity, tolerance) and what’s bad (bigotry, racial purity, authoritarianism). Not everyone was happy about it: evangelical pastors condemned the book, Pope Benedict XVI warned that it might “distort Christianity,” and it topped the American Library Association’s list of most frequently challenged books, primarily in conservative communities.


pages: 301 words: 89,076

The Globotics Upheaval: Globalisation, Robotics and the Future of Work by Richard Baldwin

agricultural Revolution, Airbnb, AltaVista, Amazon Web Services, augmented reality, autonomous vehicles, basic income, Big Tech, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, commoditize, computer vision, Corn Laws, correlation does not imply causation, Credit Default Swap, data science, David Ricardo: comparative advantage, declining real wages, deindustrialization, deskilling, Donald Trump, Douglas Hofstadter, Downton Abbey, Elon Musk, Erik Brynjolfsson, facts on the ground, Fairchild Semiconductor, future of journalism, future of work, George Gilder, Google Glasses, Google Hangouts, Hans Moravec, hiring and firing, impulse control, income inequality, industrial robot, intangible asset, Internet of things, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, knowledge worker, laissez-faire capitalism, low skilled workers, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, manufacturing employment, Mark Zuckerberg, mass immigration, mass incarceration, Metcalfe’s law, new economy, optical character recognition, pattern recognition, Ponzi scheme, post-industrial society, post-work, profit motive, remote working, reshoring, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, robotic process automation, Ronald Reagan, Salesforce, San Francisco homelessness, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, social intelligence, sovereign wealth fund, standardized shipping container, statistical model, Stephen Hawking, Steve Jobs, supply-chain management, TaskRabbit, telepresence, telepresence robot, telerobotics, Thomas Malthus, trade liberalization, universal basic income, warehouse automation

In the 1960s and 1970s, something fundamental changed that led many boys to have longer hair and many girls to have shorter hair. Using the 1950s algorithm would thus misclassify many students. The topline here is that AI-trained robots do not understand the world. They just understand patterns in their training data sets. This reliance on correlation rather than causation will inevitably lead to very systematic mistakes when underlying factors change. This is another reason AI robots are unlikely to be trusted with critical tasks. There is no danger in letting them suggests tags for your Facebook friends. There could be real danger if we fully relied on them for more essential tasks.


pages: 337 words: 87,236

Fallen Idols: Twelve Statues That Made History by Alex von Tunzelmann

anti-communist, Berlin Wall, Boris Johnson, British Empire, colonial rule, coronavirus, correlation does not imply causation, COVID-19, Donald Trump, double helix, European colonialism, Fall of the Berlin Wall, Ferguson, Missouri, George Floyd, global pandemic, Google Earth, Mahatma Gandhi, Mikhail Gorbachev, Nelson Mandela, Ronald Reagan, Scramble for Africa, Suez crisis 1956, the map is not the territory, transatlantic slave trade, W. E. B. Du Bois

‘Waging war on bronze men doesn’t make your life any more moral or just,’ the Russian journalist Maria Lipman, who cheered the destruction of Dzerzhinsky’s statue, told The New York Times in 2020. ‘It does nothing really.’3 A symbolic moment such as pulling a statue down may have resonance. What it does not do – at least, not by itself – is actually change anything. Pulling down a statue does not create liberation. Correlation is not causation. Modern movements that have targeted statues, such as Rhodes Must Fall and Black Lives Matter, have broad aims. They challenge legacies of colonialism, racism and slavery, and include strands of feminism, LGBTQ+ activism and disability activism. These movements’ focus, methods and actions are open to criticism, and there has been plenty of that – from inside the movements as well as outside.


pages: 688 words: 147,571