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Affordable Care Act / Obamacare, Black Swan, business intelligence, Carmen Reinhart, cognitive bias, correlation does not imply causation, Daniel Kahneman / Amos Tversky, 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, meta-analysis, Nate Silver, obamacare, p-value, PageRank, pattern recognition, randomized controlled trial, risk-adjusted returns, Ronald Reagan, statistical model, The Signal and the Noise by Nate Silver, Tim Cook: Apple, wikimedia commons, Yogi Berra
This book isn’t meant to be a statistics textbook. 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 t wo—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. Does the story actually use the phrase “causal” relationship? 221158 i-xiv 1-210 r4ga.indd 62 2/8/16 5:58:50 PM Are You Smarter Than an iPhone-U sing, R adiohead-L oving Republican?
So if you really want a “Proud parent of an honor roll student” bumper sticker for your minivan, apparently all you need to do is get your kids glasses and an iPhone, have them watch a few Ronald Reagan speeches, play some Radiohead, don’t let them fall asleep before midnight, turn them into lefties, and start them drinking (once they reach legal age, of course). Have we lost our minds? No. 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. We’ve seen many cases in which a finding is reported in the news as causation, even though the underlying study notes that it is only correlation.
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. 221158 i-xiv 1-210 r4ga.indd 46 2/8/16 5:58:50 PM Are You Smarter Than an iPhone-U sing, R adiohead-L oving Republican?
That is the sort of relationship that correlation looks for. 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. Consider the relationship between num_friends and daily_minutes.
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?
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, pension reform, pensions crisis, randomized controlled trial, school choice, very high income
During medical trials of new drugs, it used to be customary to record anything that happened to a patient taking an experimental drug and say the drug might have caused it: “side effects,” they were called, as it was noted that someone had a headache or a runny nose and thereafter this “side effect” was printed forever on the side of the packet. 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. The latter come to their conclusions by trying to eliminate all the other possible causes through careful control of any trial, sample, or experiment, making sure if they can that there is no bias, that samples are random when possible.
Human (and sometimes animal) ability to see how one thing leads to another is prodigious—thank goodness, since it is vital to survival. But it also goes badly wrong. 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? Not by keeping it in check—it is genius at work—but by refusing to let it sleep.
That is a cheap joke. 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. It is frighteningly easy to think in this way. Time and again someone measures a change in A, notes another in B, and declares one the mother of the other.
American Society of Civil Engineers: Report Card, Andrew Wiles, Bernie Madoff, Black Swan, call centre, correlation does not imply causation, cross-subsidies, Daniel Kahneman / Amos Tversky, edge city, Emanuel Derman, facts on the ground, 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
But imperfect information does not intimidate them; they seek models that fit the available evidence more tightly than all alternatives. 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. The greatest statistician of his generation, Sir Ronald Fisher, famously scoffed at Hill’s technique to link cigarette smoking and lung cancer; he offered that the discovery of a gene that predisposes people to both smoking and cancer would discredit such a link.
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. Adam’s breaths quickened. Therefore, Adam was a liar. Deception, or stress related to it, is only one of many possible causes for the increase in breathing rate, so variations in this or similar measures need not imply lying.
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, endowment effect, experimental subject, feminist movement, fundamental attribution error, glass ceiling, Henri Poincaré, Isaac Newton, job satisfaction, lake wobegon effect, libertarian paternalism, loss aversion, low skilled workers, Menlo Park, meta analysis, meta-analysis, quantitative easing, Richard Thaler, Ronald Reagan, Socratic dialogue, Steve Jobs, Steven Levy, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, 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 representativeness heuristic underlies many of our prior assumptions about correlation. If A is similar to B in some respect, we’re likely to see a relationship between them. 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. Reliability refers to the degree to which a case gets the same score on two occasions or when measured by different means.
A basic problem with MRA is that it typically assumes that the independent variables can be regarded as building blocks, with each variable taken by itself being logically independent of all the others. 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? Thousands, surely.
End This Depression Now! by Paul Krugman
airline deregulation, Asian financial crisis, asset-backed security, bank run, banking crisis, Bretton Woods, 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, Hyman Minsky, income inequality, inflation targeting, invisible hand, Joseph Schumpeter, Kenneth Rogoff, labour market flexibility, labour mobility, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, Long Term Capital Management, low skilled workers, Mark Zuckerberg, moral hazard, mortgage debt, paradox of thrift, price stability, quantitative easing, rent-seeking, Robert Gordon, Ronald Reagan, Upton Sinclair, We are the 99%, working poor, Works Progress Administration
Inequality and Crises Before the financial crisis of 2008 struck, I would often give talks to lay audiences about income inequality, in which I would point out that top income shares had risen to levels not seen since 1929. 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. There was a major political turn to the right in the United States, the United Kingdom, and to some extent other countries circa 1980.
Before I can answer that question, I have to talk briefly about the pitfalls one needs to avoid. 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. But suppose we look at the relationship between taxes—specifically, the share of GDP collected in federal taxes—and unemployment over the past dozen years.
., 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.
Since the rooms face each other (rather like rooms 1 and 2 in the picture shown here), and since they have been arranged to look the same from the egocentric perspective, they are actually north-side-south. 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. My proof?
There is no evidence of formal tuition in geographic coordinates at an early age (although there is evidence from Bali of some geographically relevant religious practices, such as putting children to bed with the head pointing in a particular geographic direction). 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. Two centuries after Guugu Yimithirr bequeathed “kangaroo” to the world, its last remaining speakers gave the world a harsh lesson in philosophy and psychology.
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? Or in years of close study of blue vodka?
Albert Einstein, Atul Gawande, Black Swan, 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 Ricardo: comparative advantage, Dean Kamen, delayed gratification, discounted cash flows, double entry bookkeeping, Douglas Hofstadter, en.wikipedia.org, Frederick Winslow Taylor, Gödel, Escher, Bach, high net worth, hindsight bias, index card, inventory management, iterative process, job satisfaction, Johann Wolfgang von Goethe, Kevin Kelly, Lao Tzu, loose coupling, loss aversion, market bubble, Network effects, Parkinson's law, Paul Buchheit, Paul Graham, place-making, premature optimization, Ralph Waldo Emerson, rent control, 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, 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. Did the advertisement cause the increase? Not necessarily—the increase could be due to any number of factors.
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. For example, by looking at past data you may discover trends in your sales data directly related to the date the sale was made, which is called seasonality.
Albert Einstein, AltaVista, British Empire, Cass Sunstein, cognitive dissonance, correlation does not imply causation, Daniel Kahneman / Amos Tversky, en.wikipedia.org, illegal immigration, index card, Isaac Newton, loss aversion, meta analysis, meta-analysis, mouse model, neurotypical, pattern recognition, placebo effect, Richard Thaler, Saturday Night Live, 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.
., which was delivered under the protection of four policemen. 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 order to square that circle, Kirby likened the dispute to a political campaign in which an “insurgent candidate” comes under “heavy fire from an entrenched opponent . . . the vitriol demonstrates that the challenge is being taken seriously, that it poses a realistic threat to the status quo.” 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.”) He also larded his writing with conditional statements and passive constructions: Eli Lilly “reportedly earn[ed] a profit” by licensing thimerosal to other drug companies; “the American health establishment . . . understandably has an interest in proving the unpleasant [thimerosal] theory wrong.”
airport security, availability heuristic, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, big-box store, Black Swan, Broken windows theory, Carmen Reinhart, 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, 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, George Akerlof, haute cuisine, Henri Poincaré, high batting average, housing crisis, income per capita, index fund, Internet Archive, invention of the printing press, invisible hand, Isaac Newton, James Watt: steam engine, John Nash: game theory, John von Neumann, Kenneth Rogoff, knowledge economy, locking in a profit, Loma Prieta earthquake, market bubble, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, Monroe Doctrine, mortgage debt, Nate Silver, new economy, Norbert Wiener, PageRank, pattern recognition, pets.com, prediction markets, Productivity paradox, random walk, Richard Thaler, Robert Shiller, 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 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!, wikimedia commons
However, in order to achieve that purity, it denies the need for Bayesian priors or any other sort of messy real-world context. 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. We can perhaps never know the truth with 100 percent certainty, but making correct predictions is the way to tell if we’re getting closer.
As Hatzius sees it, economic forecasters face three fundamental challenges. 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. There have been only eleven recessions since the end of World War II.26 If you have a statistical model that seeks to explain eleven outputs but has to choose from among four million inputs to do so, many of the relationships it identifies are going to be spurious.
But it has given roughly as many false alarms—including most infamously in 1984, when it sharply declined for three straight months,34 signaling a recession, but the economy continued to zoom upward at a 6 percent rate of growth. 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. If this concept is easily expressed, however, it can be hard to apply in practice, particularly when it comes to understanding the causal relationships in the economy.
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, meta analysis, meta-analysis, microbiome, New Journalism, p-value, personalized medicine, placebo effect, randomized controlled trial, the scientific method
Whom is the study about? Was the design of the study sensible? 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?
Non-normal (skewed) data can sometimes be transformed to give a normal-shape graph by plotting the logarithm of the skewed variable or performing some other mathematical transformation (such as square root or reciprocal). 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. It simply ensures that data values are given appropriate emphasis in assessing the overall effect.
I assumed this was a transcription error, so I moved the decimal point two places to the left. 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. You would be right in assuming that if two things are not correlated, it will be meaningless to attempt a regression.
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, discrete time, George Gilder, Google Earth, Infrastructure as a Service, Internet Archive, Internet of things, invisible hand, knowledge economy, late capitalism, linked data, 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. As Gould (1981: 166) notes, ‘inanimate data can never speak for themselves, and we always bring to bear some conceptual framework, either intuitive and illformed, or tightly and formally structured, to the task of investigation, analysis, and interpretation’.
In a provocative piece, Anderson argues that ‘the data deluge makes the scientific method obsolete’; that the patterns and relationships contained within big data inherently produce meaningful and insightful knowledge about social, political and economic processes and complex phenomena. He argues: There is now a better way. 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. Instead, they can mine the complete set of data for patterns that reveal effects, producing scientific conclusions without further experimentation’ (my emphasis).
As argued in Chapter 1, data are not simply natural and essential elements that are abstracted from the world in neutral and objective ways and can be accepted at face value. 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). Interpreting every correlation as meaningful can therefore lead to serious ecological fallacies. This can be exacerbated in the case of big data because the empiricist position appears to promote the practice of data dredging – hunting for every correlation – thus increasing the likelihood of discovering random associations.
Affordable Care Act / Obamacare, bank run, banking crisis, Bernie Madoff, 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, high net worth, housing crisis, invisible hand, life extension, low skilled workers, market bubble, market clearing, minimum wage unemployment, moral hazard, obamacare, price mechanism, price stability, profit maximization, quantitative easing, race to the bottom, reserve currency, risk/return, Robert Shiller, Robert Shiller, The Bell Curve by Richard Herrnstein and Charles Murray, too big to fail, transaction costs, yield curve
A clear example of the proper use of mathematical models is physics. 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. Taking these models as “gospel” is dangerous. There is also a tendency, in developing the models, to assume normal distributions with small “tails.”
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. Eventually (or soon), a flood will affect your house. The mathematical models used by economists today are often floating abstractions that are not attached to reality.
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, en.wikipedia.org, feminist movement, Florence Nightingale: pie chart, forensic accounting, global supply chain, Hans Rosling, hypertext link, invention of the telescope, inventory management, Jeff Bezos, margin call, Moneyball by Michael Lewis explains big data, Netflix Prize, p-value, performance metric, publish or perish, quantitative hedge fund, random walk, Renaissance Technologies, Robert Shiller, Robert Shiller, self-driving car, sentiment analysis, six sigma, Skype, statistical model, supply-chain management, text mining, the scientific method
The degree of relatedness is expressed as a correlation coefficient, which ranges from −1.0 to +1.0. 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.
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, AltaVista, 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, double entry bookkeeping, Eratosthenes, Erik Brynjolfsson, game design, IBM and the Holocaust, index card, informal economy, Internet of things, invention of the printing press, Jeff Bezos, Louis Pasteur, Mark Zuckerberg, Menlo Park, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, obamacare, optical character recognition, PageRank, performance metric, Peter Thiel, Post-materialism, post-materialism, random walk, recommendation engine, 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, 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.
They were able to achieve their accomplishments because so many features of the city had been datafied (however inconsistently), allowing them to process the information. The inklings of experts had to take a backseat to the data-driven approach. 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. I think it would be obtuse to think so.
Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts
affirmative action, Albert Einstein, Amazon Mechanical Turk, Black Swan, butterfly effect, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, 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, Geoffrey West, Santa Fe Institute, happiness index / gross national happiness, high batting average, hindsight bias, illegal immigration, 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, Long Term Capital Management, loss aversion, medical malpractice, meta analysis, meta-analysis, Milgram experiment, natural language processing, Netflix Prize, Network effects, oil shock, packet switching, pattern recognition, performance metric, phenotype, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, ultimatum game, urban planning, Vincenzo Peruggia: Mona Lisa, Watson beat the top human players on Jeopardy!, X Prize
With their own electronic sales databases, third-party ratings agencies like Nielsen and comScore, and the recent tidal wave of clickstream data online, advertisers can measure many more variables, and at far greater resolution, than Wanamaker could. 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.
Both these strategies will have the effect that sales and advertising will tend to be correlated whether or not the advertising is causing anything at all. 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.
The Health Gap: The Challenge of an Unequal World by Michael Marmot
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, lump of labour, Mahatma Gandhi, meta analysis, 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, 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?
As well as having health insurance, 94 per cent had graduated from high school, and 43 per cent were college graduates. The ACE study was not a one-off. 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. Indeed, further evidence supports this. Britain has been blessed by a series of long-term studies of people born at a particular moment and followed through their lives.
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. The ‘good’ characteristics of work tend to follow the social gradient: greater empowerment and better conditions go with higher status.
3D printing, Airbnb, Albert Einstein, attribution theory, augmented reality, barriers to entry, conceptual framework, correlation does not imply causation, deliberate practice, Elon Musk, Fellow of the Royal Society, Filter Bubble, Google X / Alphabet X, hive mind, index card, index fund, Isaac Newton, job satisfaction, Khan Academy, Law of Accelerating Returns, Lean Startup, Mahatma Gandhi, meta analysis, meta-analysis, pattern recognition, Peter Thiel, popular electronics, Ray Kurzweil, Richard Florida, Ronald Reagan, Saturday Night Live, self-driving car, side project, Silicon Valley, Steve Jobs
Austerity: The History of a Dangerous Idea by Mark Blyth
accounting loophole / creative accounting, balance sheet recession, bank run, banking crisis, Black Swan, Bretton Woods, capital controls, Carmen Reinhart, Celtic Tiger, central bank independence, centre right, collateralized debt obligation, correlation does not imply causation, 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 repression, fixed income, floating exchange rates, Fractional reserve banking, full employment, German hyperinflation, Gini coefficient, global reserve currency, Growth in a Time of Debt, Hyman Minsky, income inequality, interest rate swap, invisible hand, Irish property bubble, Joseph Schumpeter, Kenneth Rogoff, liquidationism / Banker’s doctrine / the Treasury view, Long Term Capital Management, market bubble, market clearing, Martin Wolf, moral hazard, mortgage debt, mortgage tax deduction, Occupy movement, offshore financial centre, paradox of thrift, price stability, quantitative easing, rent-seeking, reserve currency, road to serfdom, savings glut, short selling, structural adjustment programs, 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
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. Cut spending, raise taxes—but cut spending more than you raise taxes—and all will be well, the story went.
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.
Thinking, Fast and Slow by Daniel Kahneman
Albert Einstein, Atul Gawande, availability heuristic, 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, hindsight bias, index card, job satisfaction, John von Neumann, libertarian paternalism, loss aversion, medical residency, mental accounting, meta analysis, meta-analysis, nudge unit, pattern recognition, pre–internet, price anchoring, quantitative trading / quantitative ﬁnance, random walk, Richard Thaler, risk tolerance, Ronald Reagan, The Chicago School, The Wisdom of Crowds, 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. All stores are similar in size and merchandise selection, but their sales differ because of location, competition, and random factors.
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. This is the least-squares criterion, which is commonly accepted. Other loss functions lead to different solutions. 19: The Illusion of Understanding narrative fallacy: Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (New York: Random House, 2007).
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?
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, pattern recognition, Peter Thiel, phenotype, placebo effect, prisoner's dilemma, profit maximization, Ray Kurzweil, recommendation engine, reversible computing, Richard Feynman, Richard Feynman, Rodney Brooks, Silicon Valley, Singularitarianism, Skype, statistical model, Stephen Hawking, Steve Jobs, supervolcano, technological singularity, The Coming Technological Singularity, the scientific method, Thomas Malthus, transaction costs, Turing test, Vernor Vinge, Von Neumann architecture
If Gates is correct, then the best way to help the world’s destitute might be to figure out how to raise the IQs of the world’s poorest people. GENES VS. ENVIRONMENT Genetics determines between 50 and 80 percent of your IQ. 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.
Researchers have some decent evidence that brain training can reduce the risk of an elderly person developing dementia.278 Given the huge economic burden that dementia imposes on the United States, if brain training proved effective, it could reduce the rate of increase of Medicare costs. 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. Researchers could use this data to figure out what types of exercises worked best for different categories of students.
The Future of the Brain: Essays by the World's Leading Neuroscientists by Gary Marcus, Jeremy Freeman
23andMe, Albert Einstein, bioinformatics, bitcoin, brain emulation, cloud computing, complexity theory, computer age, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data acquisition, Drosophila, epigenetics, Google Glasses, iterative process, linked data, mouse model, optical character recognition, pattern recognition, personalized medicine, phenotype, race to the bottom, Richard Feynman, Richard Feynman, Ronald Reagan, semantic web, speech recognition, stem cell, Steven Pinker, supply-chain management, Turing machine, web application
Furthermore, experiments with appropriately modified viruses to stain, mark, turn on, or turn off molecularly identified subpopulations of neurons permit unprecedented control of mouse brain circuitry. 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 blurriness of these instruments was mirrored by the primitive and edentate tools used to safely perturb the human brain—electrical stimulation in patients, and extracranial electromagnetic fields and drugs in volunteers. 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.
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, hive mind, impulse control, information retrieval, Isaac Newton, Jaron Lanier, John von Neumann, Kevin Kelly, mandelbrot fractal, market design, Mars Rover, Marshall McLuhan, microbiome, Murray Gell-Mann, Nicholas Carr, open economy, place-making, placebo effect, pre–internet, QWERTY keyboard, random walk, randomized controlled trial, rent control, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, Schrödinger's Cat, security theater, Silicon Valley, stem cell, Steve Jobs, Steven Pinker, Stewart Brand, the scientific method, Thorstein Veblen, Turing complete, Turing machine, Walter Mischel, Whole Earth Catalog
If, for most reasonable sets of priors, information about A would allow us to update our estimate of B, then it would seem there is some sort of causal connection between the two. 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.
What the F: What Swearing Reveals About Our Language, Our Brains, and Ourselves by Benjamin K. Bergen
For example, here’s a graph of the age at which one particular child first used each of his nouns (his age is on the x-axis) plotted against how frequent that word was in the child-directed speech he heard (it’s actually the log of word frequency because frequency effects in language have logarithmic effects).21 You can see that within nouns, the child learns more frequent ones earlier, on average, and then moves on to learn less frequent ones as well. 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. Maybe frequency plays no causal role. To know for sure, you’d need to run an experiment: you’d have to manipulate how often children heard particular words and see whether this factor alone, holding all other possible causes constant, affected children’s learning of the words.
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.
Affordable Care Act / Obamacare, 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, Emanuel Derman, housing crisis, illegal immigration, Internet of things, late fees, 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, Sharpe ratio, statistical model, Tim Cook: Apple, too big to fail, Unsafe at Any Speed, Upton Sinclair, Watson beat the top human players on Jeopardy!, working poor
Insurance companies as well as bankers delineated neighborhoods where they would not invest. 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). We’ve taken out loans and seen how our creditworthiness is a stand-in for other virtues or vices.
The Euro: How a Common Currency Threatens the Future of Europe by Joseph E. Stiglitz, Alex Hyde-White
bank run, banking crisis, barriers to entry, battle of ideas, Berlin Wall, Bretton Woods, 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, investor state dispute settlement, invisible hand, Kenneth Rogoff, knowledge economy, labour market flexibility, labour mobility, manufacturing employment, market bubble, market friction, market fundamentalism, Martin Wolf, Mexican peso crisis / tequila crisis, 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 Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, secular stagnation, Silicon Valley, sovereign wealth fund, the payments system, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, transfer pricing, trickle-down economics, Washington Consensus, working-age population
Supporters of the euro might respond by pointing out that if Greece owed money to, say, Germany in Germany’s currency, the weakening of Greece’s exchange rate would increase the real indebtedness of Greece. 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. That’s why Germany’s suggestion that the failures of the countries in the eurozone are due to their profligacy seems so out of touch with economic reality, so demonstrative of a total lack of analysis.
They argued that there were important instances where when governments had contracted government spending, the result was that the overall economy grew. 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. Canada benefited, too from a flexible exchange rate that enabled it to sell its goods more cheaply to the United States.
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, Bonfire of the Vanities, British Empire, Broken windows theory, California gold rush, Cass Sunstein, citation needed, clean water, cognitive dissonance, colonial rule, Columbine, computer age, 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, 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, ghettoisation, Gini coefficient, global village, Henri Poincaré, impulse control, income inequality, informal economy, invention of the printing press, Isaac Newton, lake wobegon effect, libertarian paternalism, loss aversion, Marshall McLuhan, McMansion, means of production, mental accounting, meta analysis, meta-analysis, Mikhail Gorbachev, mutually assured destruction, 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, statistical model, stem cell, Steven Levy, Steven Pinker, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, theory of mind, transatlantic slave trade, transatlantic slave trade, Turing machine, ultimatum game, uranium enrichment, V2 rocket, Walter Mischel, WikiLeaks, women in the workforce
When rock music burst onto the scene in the 1950s, politicians and clergymen vilified it for corrupting morals and encouraging lawlessness. (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.
Archer found that countries in which women are better represented in government and the professions, and in which they earn a larger proportion of earned income, are less likely to have women at the receiving end of spousal abuse. 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. Though elsewhere I have been chary about making predictions, I think it’s extremely likely that in the coming decades violence against women will decrease throughout the world.
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.
The Collapse of Western Civilization: A View From the Future by Naomi Oreskes, Erik M. Conway
anti-communist, correlation does not imply causation, en.wikipedia.org, energy transition, invisible hand, laissez-faire capitalism, market fundamentalism, means of production, oil shale / tar sands, 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.
Wall Street: How It Works And for Whom by Doug Henwood
accounting loophole / creative accounting, affirmative action, Andrei Shleifer, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, borderless world, Bretton Woods, British Empire, capital asset pricing model, capital controls, central bank independence, corporate governance, correlation coefficient, correlation does not imply causation, credit crunch, currency manipulation / currency intervention, David Ricardo: comparative advantage, debt deflation, declining real wages, deindustrialization, dematerialisation, diversification, diversified portfolio, Donald Trump, equity premium, Eugene Fama: efficient market hypothesis, experimental subject, facts on the ground, financial deregulation, 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, interest rate swap, Internet Archive, invisible hand, Isaac Newton, joint-stock company, Joseph Schumpeter, kremlinology, labor-force participation, late capitalism, law of one price, liquidationism / Banker’s doctrine / the Treasury view, London Interbank Offered Rate, Louis Bachelier, market bubble, Mexican peso crisis / tequila crisis, microcredit, minimum wage unemployment, moral hazard, mortgage debt, mortgage tax deduction, oil shock, payday loans, pension reform, Plutocrats, plutocrats, price mechanism, price stability, prisoner's dilemma, profit maximization, Ralph Nader, random walk, reserve currency, Richard Thaler, risk tolerance, Robert Gordon, Robert Shiller, Robert Shiller, shareholder value, short selling, Slavoj Žižek, South Sea Bubble, 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
A policy-led decline in interest rates pushes up stock prices, and stockholding households spend more. 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. U.S. theories of, 137 function, 59 as "fundamental" (Marx), 244 information asymmetry, 172 market share by lending institution, 81 structure, 58-62 subordination to production (Marx), 237 U.S. international position, 61 see also bond markets; debt; money, psychology of credit crunch 0989-92), 158-161 credit gratuitiVioudhon), 302 credit rationing, 172 in Keynes's Treatise, 193-194 crime, business, 252 crises, corporate, financial interests assert power during, 265 crises, financial, 265 financiers' political uses of, 294-297 increasing prominence starting in 1970s, 222 money and, 93-94 Keynes, 202-205 Marx, 232-236 Third World, 110, 294-295 see also bailouts Crotty, James, 229 crowd psychology, 176-177, 185 currency markets, 41^9 crises, economic causes, 44 gold, 46-49 history, 41^4 mechanics and trading volume, 45—46 during trading week, 130-131 underlying values, 44-45 currency swaps, 35 Dale, James Davidson, 104 Davidson, Paul, 242, 243 Debreu, Gerard, 139 debt appropriate underlying assets, 247 as conservatizing force, 66 ideal level, pre-MM, 150 and 1930s depression, 155-158 and political power, 4, 23 reasons to shun, 149 by sector, 58-59 by type (table), 60 see also credit/credit markets; specific sectors debt deflation (Fisher), 157 modern absence of, 234-235 why there was none in early 1990s, 158-161 deficit financing, 297 deflations.
Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, call centre, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, en.wikipedia.org, Erik Brynjolfsson, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, 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, software as a service, speech recognition, statistical model, Steven Levy, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra
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!
Doing Good Better: How Effective Altruism Can Help You Make a Difference by William MacAskill
barriers to entry, 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, effective altruism, en.wikipedia.org, experimental subject, follow your passion, food miles, immigration reform, income inequality, index fund, Isaac Newton, job automation, job satisfaction, labour mobility, Lean Startup, M-Pesa, meta analysis, 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 Wealth of Nations by Adam Smith, universal basic income, women in the workforce
Even among the “bottom billion”—the population of countries that have experienced the weakest economic growth over the last few decades—quality of life has increased dramatically. 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. But in fact there’s good reason to think that, on average, international aid spending has been incredibly beneficial.
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). Intuitively, microcredit seems like it would be very cost-effective, and there were many anecdotes of people who’d received microloans and used them to start businesses that, in turn, helped them escape poverty.
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, special economic zone, transatlantic slave trade, transatlantic slave trade, women in the workforce
The methodology of such studies is typically weak, and it doesn’t adequately account for cause and effect. “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. Likewise, two of the places in the Arab world that have given girls the most education were Lebanon and Saudi Arabia, yet the former has been a vortex of conflict and the latter a breeding ground for violent fundamentalists.
This was something that we didn’t expect at all. It shows the power of education.” 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. As study after study has taught us, there is no tool for development more effective than the empowerment of women.
23andMe, airport security, Albert Einstein, Black Swan, Buckminster Fuller, carbon footprint, cognitive dissonance, Columbine, correlation does not imply causation, Dean Kamen, game design, Gary Taubes, index card, Kevin Kelly, knowledge economy, life extension, Mahatma Gandhi, microbiome, p-value, Parkinson's law, Paul Buchheit, placebo effect, Productivity paradox, publish or perish, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Richard Feynman, Silicon Valley, Silicon Valley startup, Skype, stem cell, Steve Jobs, Thorstein Veblen, wage slave, William of Occam
The point isn’t to speculate about hundreds of possible explanations. The point is to be skeptical, especially of sensationalist headlines. 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. Eating more, then less, then more, and so on in a continuous sine wave is an impulse we can leverage to reach goals faster.
Here is the most important paragraph in this chapter: Observational studies cannot control or even document all of the variables involved. 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. Coincidence? Drawing unwarranted cause-and-effect conclusions from observational studies is the bread-and-butter of media and cause- or financially-driven scientists blind to their own lack of ethics.
Then try to bundle all the data up together, so that your negative data is swallowed up by some mediocre positive results. 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. If so, then leave it like that.
The New Kingmakers by Stephen O'Grady
Amazon Web Services, barriers to entry, cloud computing, correlation does not imply causation, crowdsourcing, DevOps, Jeff Bezos, Khan Academy, Kickstarter, Mark Zuckerberg, Netflix Prize, Paul Graham, Silicon Valley, Skype, software as a service, software is eating the world, Steve Ballmer, Steve Jobs, Tim Cook: Apple, Y Combinator
Seventeen months into its existence, Android was an interesting project, but an also-ran next to Apple’s iPhone OS (it was not renamed iOS until June 2010). 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). By the time the conference rolled around again a year later, the number was around 100,000.
Terms of Service: Social Media and the Price of Constant Connection by Jacob Silverman
23andMe, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, Brian Krebs, California gold rush, call centre, cloud computing, cognitive dissonance, correlation does not imply causation, Credit Default Swap, crowdsourcing, don't be evil, Edward Snowden, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, information retrieval, Internet of things, Jaron Lanier, jimmy wales, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, late capitalism, license plate recognition, life extension, Lyft, Mark Zuckerberg, Mars Rover, Marshall McLuhan, meta analysis, meta-analysis, Minecraft, 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, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Snapchat, social graph, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, TaskRabbit, technoutopianism, telemarketer, transportation-network company, Turing test, Uber and Lyft, Uber for X, universal basic income, unpaid internship, women in the workforce, Y Combinator, 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. But data itself can be messy and often must be smoothed over, interpreted, supplemented.
., 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.
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, Isaac Newton, job automation, job satisfaction, McMansion, natural language processing, PageRank, personalized medicine, recommendation engine, RFID, Silicon Valley, Skype, statistical model, Watson beat the top human players on Jeopardy!
A man can write like a woman, he says, but does he buy like a woman? 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. If they screw up, the only harm is that we see the wrong ad or receive irrelevant coupons.
A Mathematician Plays the Stock Market by John Allen Paulos
Benoit Mandelbrot, Black-Scholes formula, Brownian motion, business climate, butterfly effect, capital asset pricing model, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, diversified portfolio, 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, invisible hand, Isaac Newton, John Nash: game theory, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, mental accounting, Nash equilibrium, Network effects, passive investing, Paul Erdős, Ponzi scheme, price anchoring, Ralph Nelson Elliott, random walk, Richard Thaler, Robert Shiller, Robert Shiller, short selling, six sigma, Stephen Hawking, transaction costs, 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. Given the above distinction, some of you may wish to skip the next three paragraphs on the calculation of covariance.
Albert Einstein, Benoit Mandelbrot, correlation does not imply causation, 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, stem cell
What about all those headlines trumpeting the discovery of a gene for everything from depression to schizophrenia? 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. But does the key actually “control” the car? If it did, you couldn’t leave the key in the car alone because it might just borrow your car for a joy ride when you are not paying attention.
Mastering Pandas by Femi Anthony
Amazon Web Services, correlation coefficient, correlation does not imply causation, Debian, en.wikipedia.org, Internet of things, natural language processing, p-value, random walk, side project, statistical model
Correlation is the general term we use in statistics for variables that express dependence with each other. 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. The correlation measure, known as correlation coefficient, is a number that captures the size and direction of the relationship between the two variables.
Albert Einstein, Build a better mousetrap, Burning Man, cognitive bias, correlation does not imply causation, deskilling, fear of failure, Mahatma Gandhi, Mark Zuckerberg, school choice, Silicon Valley, The Wealth of Nations by Adam Smith
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. A similar fallacy—that an event that follows another was a consequence of the first—is described as post hoc ergo propter hoc (Latin for “after this, therefore because of this”).
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.
The Organized Mind: Thinking Straight in the Age of Information Overload by Daniel J. Levitin
airport security, Albert Einstein, Amazon Mechanical Turk, Anton Chekhov, 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, 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, impulse control, index card, indoor plumbing, information retrieval, invention of writing, iterative process, jimmy wales, job satisfaction, Kickstarter, life extension, meta analysis, meta-analysis, more computing power than Apollo, Network effects, new economy, Nicholas Carr, optical character recognition, pattern recognition, phenotype, placebo effect, pre–internet, profit motive, randomized controlled trial, Skype, Snapchat, statistical model, Steve Jobs, supply-chain management, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Turing test, ultimatum game
The results of Harvard’s salary survey are no doubt intended to lead the average person to infer that a Harvard education is responsible for the high salaries of recent graduates. 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. With industrialization came air travel and air cargo; larger, better fortified ships; and improved security and policing practices.
Data Scientists at Work by Sebastian Gutierrez
Albert Einstein, algorithmic trading, bioinformatics, bitcoin, business intelligence, chief data officer, clean water, cloud computing, computer vision, continuous integration, correlation does not imply causation, crowdsourcing, data is the new oil, DevOps, domain-specific language, follow your passion, full text search, informal economy, information retrieval, Infrastructure as a Service, inventory management, iterative process, linked data, Mark Zuckerberg, microbiome, Moneyball by Michael Lewis explains big data, move fast and break things, natural language processing, Network effects, nuclear winter, optical character recognition, pattern recognition, Paul Graham, personalized medicine, Peter Thiel, pre–internet, quantitative hedge fund, quantitative trading / quantitative ﬁnance, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman, self-driving car, side project, Silicon Valley, Skype, software as a service, speech recognition, statistical model, Steve Jobs, stochastic process, technology bubble, text mining, the scientific method, web application
That’s why Tukey was writing about it in 1962 when he was ordering everybody to reorient statistics as a professional discipline and a funding line for the NSF organized around computation and data and data analysis. He wrote an article in 1962 called “The Future of Data Analysis.”13 And he wasn’t the last, right? 10 http://vserver1.cscs.lsa.umich.edu/~crshalizi/reviews/fragile-objects/ Wright, Sewall.“Correlation and causation.” Journal of Agricultural Research 20.7 (1921), 557-585. 12 Herbert Robbins and Sutton Monro, “A Stochastic Approximation Method”: Ann. Math. Statist.,Volume 22, Number 3 (1951), 400-407. 13 John W. Tukey, “The Future of Data Analysis”: Ann. Math. Statist.,Volume 33, Number1 (1962),1-67. 11 www.it-ebooks.info Data Scientists at Work Leo Breiman all throughout the 1990s was writing to his community of statisticians, “Let us get with data, statistics community!”
There are lots of challenges working with data in the algorithmic world. One of them is that thousands and thousands of other people are looking at the exact same data sets, and basically they’re all just squeezing everything they possibly can out of it. Another challenge is that people under pressure to find patterns are prone to fall into the common human fallacies of overfitting models with insufficient data and overreading correlation as causation. Gutierrez: How do the data challenges you faced in the algorithmic trading world compare to the data challenges you face at PlaceIQ? Lenaghan: The initial data challenge when I came to PlaceIQ was that geospatial data was a data type that I had never worked with. The second challenge was that the data volume was scaled up by a couple of orders of magnitude. The volume of data in the algorithmic trading I was doing was quite large—say, a terabyte a year.
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, 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, income per capita, invisible hand, James Watt: steam engine, Jane Jacobs, John Snow's cholera map, Joseph Schumpeter, Kenneth Rogoff, M-Pesa, microcredit, Monroe Doctrine, oil shock, place-making, Ponzi scheme, risk/return, road to serfdom, Silicon Valley, Steve Jobs, The Death and Life of Great American Cities, The Wealth of Nations by Adam Smith, Thomas L Friedman, urban planning, urban renewal, Washington Consensus, World Values Survey, young professional
42 As already mentioned, this survey does not meet the standards of hard evidence (which does not exist on the autocracy-versus-freedom issue either way). The survey does provide a rare opportunity for poor people to speak for themselves. 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. Some who favor technocratic approaches disqualify any discussion of rights because the evidence for positive consequences of rights is not rigorous enough.
And we also saw already what we see more evidence for here: a history of autocracy and violence breeds more lack of trust. 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.
The Googlization of Everything: by Siva Vaidhyanathan
1960s counterculture, 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, data acquisition, death of newspapers, don't be evil, Firefox, Francis Fukuyama: the end of history, full text search, global village, Google Earth, Howard Rheingold, informal economy, information retrieval, Joseph Schumpeter, Kevin Kelly, knowledge worker, libertarian paternalism, market fundamentalism, Marshall McLuhan, means of production, Mikhail Gorbachev, Naomi Klein, Network effects, new economy, Nicholas Carr, PageRank, pirate software, Ray Kurzweil, Richard Thaler, Ronald Reagan, side project, Silicon Valley, Silicon Valley ideology, single-payer health, Skype, social web, Steven Levy, Stewart Brand, technoutopianism, The Nature of the Firm, The Structural Transformation of the Public Sphere, Thorstein Veblen, urban decay, web application
And Google was right.40 Needless to say, Anderson’s techno-fundamentalist hyperbole belies a vested interest in the narrative of the revolutionary and transformational power of computing. 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. Given the passionate promotion of such computational models for science of all types, we run the risk of diverting precious research funding and initiatives away from the hard, expensive, painstaking laboratory science that has worked so brilliantly for three centuries.
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, McMansion, meta analysis, meta-analysis, out of africa, peak oil, placebo effect, Rosa Parks, the built environment
They may suggest intriguing areas for exploration but until all the variables are controlled and the results are reproducible, no conclusions can be drawn. 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. John Yudkin’s 1957 study shows the error of conflating correlation with causation. You can see from Figure 4B (page over) that 163 Nutritional Vegetarians 8 US 7 Canada Australia CHD, Deaths per 1000 6 5 England and Wales 4 3 2 Italy 1 0 Japan 0 10 20 30 40 50 Percent Calories from Fat 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. 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.
Bad Science by Ben Goldacre
Asperger Syndrome, correlation does not imply causation, experimental subject, Ignaz Semmelweis: hand washing, John Snow's cholera map, Louis Pasteur, meta analysis, meta-analysis, offshore financial centre, p-value, placebo effect, Richard Feynman, Richard Feynman, risk tolerance, Ronald Reagan, the scientific method, urban planning
Or you could get really serious, and start to manipulate the statistics. For two pages only, this book will now get quite nerdy. 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. If so, then leave it like that.
Free Ride by Robert Levine
A Declaration of the Independence of Cyberspace, Anne Wojcicki, book scanning, borderless world, Buckminster Fuller, citizen journalism, correlation does not imply causation, crowdsourcing, death of newspapers, Edward Lloyd's coffeehouse, Firefox, future of journalism, Googley, Hacker Ethic, informal economy, Jaron Lanier, Julian Assange, Kevin Kelly, linear programming, 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
Asked about the study, Andersen says the Pirate Bay file-sharing service doesn’t offer much copyrighted music from major-label acts—although even a cursory glance at its Web site shows that isn’t true. 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. In Spain, where fully 45 percent of Internet users get media from pirate services—about twice the average rate in Europe76—CD sales declined 77 percent since 2001, compared with a continent-wide average decline of 54 percent.77 And Japan is now the No. 1 market for CDs, despite having one-third the population of the United States, partly because many people there access the Internet with mobile phones that don’t run file-sharing programs.78 In June 2010, with U.S. music sales less than half what they were a decade earlier, Oberholzer-Gee and Strumpf published another paper which conceded that file sharing had affected sales, but not that much and in a way that helped society.79 They said that 60 percent of online traffic consisted of file trading—an astonishing statistic that implies illegal downloading accounts for more than half of all Internet use—but that all this piracy accounted for 20 percent or less of the decline in music sales.
Culture & Empire: Digital Revolution by Pieter Hintjens
4chan, airport security, anti-communist, anti-pattern, barriers to entry, Bill Duvall, bitcoin, blockchain, business climate, business intelligence, business process, Chelsea Manning, clean water, congestion charging, Corn Laws, correlation does not imply causation, cryptocurrency, Debian, Edward Snowden, failed state, financial independence, Firefox, full text search, German hyperinflation, global village, GnuPG, Google Chrome, greed is good, Hernando de Soto, hiring and firing, informal economy, invisible hand, James Watt: steam engine, Jeff Rulifson, Julian Assange, Kickstarter, M-Pesa, mutually assured destruction, Naomi Klein, national security letter, 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 Feynman, Richard Stallman, Satoshi Nakamoto, security theater, 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, union organizing, web application, WikiLeaks, Y2K, zero day, Zipf's Law
And yet, when people stopped talking about religions, and instead looked at the politics, we found solutions. This is a consistent pattern. 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. It appeals to atheists and Christians alike, and provides convenient cover, both for unprecedented profit-taking, and for creating the spy networks.
Made to Stick: Why Some Ideas Survive and Others Die by Chip Heath, Dan Heath
affirmative action, availability heuristic, Barry Marshall: ulcers, correlation does not imply causation, desegregation, Menlo Park, Ronald Reagan, Rosa Parks, shareholder value, Silicon Valley, Stephen Hawking, telemarketer
Marshall and Warren could not even get their research paper accepted by a medical journal. 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. One morning he skipped breakfast and asked his colleagues to meet him in the lab.
In Defense of Global Capitalism by Johan Norberg
Asian financial crisis, capital controls, clean water, correlation does not imply causation, 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, informal economy, Joseph Schumpeter, Kenneth Rogoff, land reform, Lao Tzu, manufacturing employment, market fundamentalism, Mexican peso crisis / tequila crisis, Naomi Klein, new economy, open economy, profit motive, race to the bottom, rising living standards, school vouchers, 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, union organizing
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.
Airbnb, bank run, banks create money, Bernie Madoff, bitcoin, Bretton Woods, Carmen Reinhart, correlation does not imply causation, Credit Default Swap, crony capitalism, crowdsourcing, Donald Trump, Downton Abbey, fiat currency, financial innovation, Fractional reserve banking, full employment, George Gilder, Home mortgage interest deduction, Jeff Bezos, job automation, Joseph Schumpeter, Kenneth Rogoff, Kickstarter, liquidity trap, Mark Zuckerberg, market bubble, moral hazard, mortgage tax deduction, NetJets, offshore financial centre, oil shock, peak oil, Peter Thiel, 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, 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.
Asperger Syndrome, Barry Marshall: ulcers, Berlin Wall, biofilm, clean water, correlation does not imply causation, discovery of penicillin, Drosophila, Fall of the Berlin Wall, friendly fire, germ theory of disease, Ignaz Semmelweis: hand washing, illegal immigration, John Snow's cholera map, Louis Pasteur, Maui Hawaii, meta analysis, meta-analysis, microbiome, phenotype, placebo effect, the scientific method
The data included information about antibiotic use when the children were infants. 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. In fact, the number of hours in front of a television was being used as a proxy for the amount of exercise children were getting.
It took time for antibiotics to reach common usage, for further antibiotic drugs to be developed, for children to grow up with the influence of these drugs on their bodies, and for chronic diseases to develop in their own insidious way. 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.
Republic, Lost: How Money Corrupts Congress--And a Plan to Stop It by Lawrence Lessig
asset-backed security, banking crisis, carried interest, cognitive dissonance, corporate personhood, correlation does not imply causation, crony capitalism, David Brooks, Edward Glaeser, Filter Bubble, financial deregulation, financial innovation, financial intermediation, invisible hand, jimmy wales, Martin Wolf, meta analysis, meta-analysis, Mikhail Gorbachev, moral hazard, place-making, profit maximization, Ralph Nader, regulatory arbitrage, rent-seeking, Ronald Reagan, Silicon Valley, single-payer health, The Wealth of Nations by Adam Smith, too big to fail, upwardly mobile, WikiLeaks, 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.
3D printing, Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, crowdsourcing, Danny Hillis, data is the new oil, double helix, Douglas Hofstadter, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, global village, Google Glasses, Gödel, Escher, Bach, information retrieval, job automation, 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, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, Second Machine Age, self-driving car, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight
This technique, called A/B testing, was at first used mainly in drug trials but has since spread to many fields where data can be gathered on demand, from marketing to foreign aid. 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.
Come as You Are: The Surprising New Science That Will Transform Your Sex Life by Emily Nagoski Ph.d.
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.
The New Division of Labor: How Computers Are Creating the Next Job Market by Frank Levy, Richard J. Murnane
Atul Gawande, call centre, computer age, correlation does not imply causation, David Ricardo: comparative advantage, deskilling, Frank Levy and Richard Murnane: The New Division of Labor, hypertext link, index card, job automation, knowledge economy, knowledge worker, low skilled workers, low-wage service sector, pattern recognition, profit motive, Robert Shiller, Robert Shiller, Ronald Reagan, speech recognition, talking drums, telemarketer, The Wealth of Nations by Adam Smith, working poor
The pattern for routine manual tasks—tasks that might be subsumed by automation—is roughly similar: a slight rise during the 1970s and a steady decline in the subsequent two decades. The share of the labor force working in occupations that emphasize nonroutine manual tasks declined throughout the period. This reﬂects in part the movement of manufacturing jobs offshore. The data in ﬁgures 3.2 (occupations) and 3.5 (tasks) are consistent with our description of computers’ economic impacts. But this correlation does not prove causation—the trend in both ﬁgures could have been caused by other factors. To make a stronger case, we must increase the level of detail to look at changes within industries. If our argument is right—if the adoption of computers shifts work away from routine tasks and toward tasks requiring expert thinking and complex communication—it should be observable when we look within industries. Speciﬁcally, we can ask: are those industries that invested most heavily in computers the industries where we see the greatest changes in task structure?
Albert Einstein, anti-communist, Brownian motion, correlation does not imply causation, Dmitri Mendeleev, Ernest Rutherford, Fellow of the Royal Society, Gary Taubes, Isaac Newton, John von Neumann, Mikhail Gorbachev, Project Plowshare, Richard Feynman, Richard Feynman, Ronald Reagan, the scientific method, Yom Kippur War
The Skeptical Economist: Revealing the Ethics Inside Economics by Jonathan Aldred
airport security, Berlin Wall, carbon footprint, citizen journalism, clean water, cognitive dissonance, congestion charging, correlation does not imply causation, Diane Coyle, experimental subject, Fall of the Berlin Wall, first-past-the-post, framing effect, greed is good, happiness index / gross national happiness, invisible hand, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, labour market flexibility, laissez-faire capitalism, libertarian paternalism, new economy, pension reform, positional goods, Ralph Waldo Emerson, RAND corporation, risk tolerance, school choice, spectrum auction, trade liberalization, ultimatum game
And if it can, is that what these surveys are measuring? With its breezy optimism the happiness experts’ reading of the evidence ignores some awkward objections. To begin with, it is easy to pick holes in the neuroscience. As ever, the problem is not the science itself, but the interpretative spin put on the results. To begin with, the core of the neuroscientific research is a set of correlations which do not demonstrate any causation. There is little understanding of why external stimuli are associated with increased brain activity, so there is no basis for assuming causation. And even the correlations are less robust than they appear, because of the assumptions which have been made to derive them. For instance, most of the research adopts the ‘subtractive method’, in which measurements of brain activity in the control condition (when there is no stimulus) are subtracted from measurements in the experimental condition (when the stimulus is present).
The Cost of Inequality: Why Economic Equality Is Essential for Recovery by Stewart Lansley
banking crisis, Basel III, Big bang: deregulation of the City of London, Bonfire of the Vanities, borderless world, Branko Milanovic, Bretton Woods, British Empire, business process, call centre, capital controls, collective bargaining, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, crony capitalism, David Ricardo: comparative advantage, deindustrialization, Edward Glaeser, falling living standards, financial deregulation, financial innovation, Financial Instability Hypothesis, floating exchange rates, full employment, Goldman Sachs: Vampire Squid, high net worth, hiring and firing, Hyman Minsky, income inequality, James Dyson, Jeff Bezos, job automation, Joseph Schumpeter, Kenneth Rogoff, knowledge economy, laissez-faire capitalism, Long Term Capital Management, low skilled workers, manufacturing employment, market bubble, Martin Wolf, mittelstand, mobile money, Mont Pelerin Society, new economy, Nick Leeson, North Sea oil, Northern Rock, offshore financial centre, oil shock, Plutocrats, plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, rising living standards, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, shareholder value, The Great Moderation, The Spirit Level, The Wealth of Nations by Adam Smith, Thomas Malthus, too big to fail, Tyler Cowen: Great Stagnation, Washington Consensus, Winter of Discontent, working-age population
‘I could hardly believe how tight the fit was—it was a stunning correlation,’ Moss told the New York Times. ‘And it began to raise the question of whether there are causal links between financial deregulation, economic inequality and instability.’240 Of course, as Moss has accepted, correlation is not the same as causation. As one of his critics, R Glenn Hubbard, dean of the Columbian Business School and top economic adviser to former President George W Bush has put it, ‘Cars go faster every year, and GDP rises every year, but that doesn’t mean speed causes GDP.’ 241 The correlation could mean that the direction of causation is from slump to inequality. Yet what is significant about this pattern is that in both the 1920s and the pre-2007 period, inequality rose sharply in the years before recession took hold. There is now an increasing, if still small, body of academics that have attributed the crisis at least in part to rising inequality.
3D printing, Ada Lovelace, AI winter, Airbnb, artificial general intelligence, augmented reality, barriers to entry, bitcoin, blockchain, brain emulation, Buckminster Fuller, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, dematerialisation, discovery of the americas, disintermediation, don't be evil, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Google Glasses, industrial robot, Internet of things, invention of agriculture, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, life extension, low skilled workers, Mahatma Gandhi, means of production, mutually assured destruction, Nicholas Carr, pattern recognition, Peter Thiel, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, South Sea Bubble, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Jobs, strong AI, technological singularity, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E
The book points out some interesting unexpected side-effects of Big Data. It turns out that having more data beats having better data, if what you want is to be able to understand, predict and influence the behaviour of large numbers of people. It also turns out that if you find a reliable correlation then it often doesn’t matter if there is a causal link between the two phenomena. We all know of cases where correlation has been mistaken for causation and ineffective or counter-productive policies have been imposed as a result. But if a correlation persists long enough it may provide decision-makers with a useful early warning signal. For instance, a supermarket and an insurance company shared data sets and discovered that men buying red meat and milk during the day were better insurance risks than men buying pasta and petrol late at night.
Collapse: How Societies Choose to Fail or Succeed by Jared Diamond
clean water, colonial rule, correlation does not imply causation, cuban missile crisis, Donner party, European colonialism, Exxon Valdez, illegal immigration, job satisfaction, means of production, new economy, North Sea oil, Piper Alpha, polynesian navigation, profit motive, South Sea Bubble, statistical model, Stewart Brand, Thomas Malthus, trade route, transcontinental railway, unemployed young men
Among McGovern’s papers are Thomas McGovern, “The Vinland adventure: a North Atlantic perspective” (North American Archaeologist 2:285-308 (1981)); Thomas McGovern, “Contributions to the paleoeconomy of Norse Greenland” (Acta Archaeologica 54:73-122 (1985)); Thomas McGovern et al., “Northern islands, human era, and environmental degradation: a view of social and ecological change in the medieval North Atlantic” (Human Ecology 16:225-270 (1988)); Thomas McGovern, “Climate, correlation, and causation in Norse Greenland” (Arctic Anthropology 28:77-100 (1991)); Thomas McGovern et al., “A vertebrate zooarchaeology of Sandnes V51: economic change at a chieftain’s farm in West Greenland” (Arctic Anthropology 33:94-121 (1996)); Thomas Amorosi et al., “Raiding the landscape: human impact from the Scandinavian North Atlantic” (Human Ecology 25:491-518 (1997)); and Tom Amorosi et al., “They did not live by grass alone: the politics and paleoecology of animal fodder in the North Atlantic region” (Environmental Archaeology 1:41-54 (1998)).
Collapse by Jared Diamond
clean water, colonial rule, correlation does not imply causation, cuban missile crisis, Donner party, European colonialism, Exxon Valdez, illegal immigration, job satisfaction, means of production, new economy, North Sea oil, Piper Alpha, polynesian navigation, prisoner's dilemma, South Sea Bubble, statistical model, Stewart Brand, Thomas Malthus, trade route, transcontinental railway, unemployed young men
Among McGovern's papers are Thomas McGovern, "The Vinland adventure: a North Atlantic perspective" (North American Archaeologist 2:285-308 (1981)); Thomas McGovern, "Contributions to the paleoeconomy of Norse Greenland" (Acta Archaeologica 54:73-122 (1985)); Thomas McGovern et al., "Northern islands, human era, and environmental degradation: a view of social and ecological change in the medieval North Atlantic" (Human Ecology 16:225-270 (1988)); Thomas McGovern, "Climate, correlation, and causation in Norse Greenland" (Arctic Anthropology 28:77-100 (1991)); Thomas McGovern et al., "A vertebrate zooarchaeology of Sandnes V51: economic change at a chieftain's farm in West Greenland" (Arctic Anthropology 33:94-121 (1996)); Thomas Amorosi et al, "Raiding the landscape: human impact from the Scandinavian North Atlantic" (Human Ecology 25:491-518 (1997)); and Tom Amorosi et al, "They did not live by grass alone: the politics and paleoecology of animal fodder in the North Atlantic region" (Environmental Archaeology 1:41-54 (1998)).
Stress Test: Reflections on Financial Crises by Timothy F. Geithner
Affordable Care Act / Obamacare, asset-backed security, Atul Gawande, bank run, banking crisis, Basel III, Bernie Madoff, Bernie Sanders, Buckminster Fuller, Carmen Reinhart, central bank independence, collateralized debt obligation, correlation does not imply causation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, David Brooks, Doomsday Book, eurozone crisis, financial innovation, Flash crash, Goldman Sachs: Vampire Squid, housing crisis, Hyman Minsky, illegal immigration, implied volatility, London Interbank Offered Rate, Long Term Capital Management, margin call, market fundamentalism, Martin Wolf, McMansion, Mexican peso crisis / tequila crisis, moral hazard, mortgage debt, Nate Silver, Northern Rock, obamacare, paradox of thrift, pets.com, price stability, profit maximization, pushing on a string, quantitative easing, race to the bottom, RAND corporation, regulatory arbitrage, reserve currency, Saturday Night Live, savings glut, short selling, sovereign wealth fund, The Great Moderation, The Signal and the Noise by Nate Silver, Tobin tax, too big to fail, working poor
I helped call more attention to the vulnerabilities that come from risky forms of financing, alongside the IMF’s traditional focus on large fiscal deficits and inflation. And while the Bush team liked to criticize the bailouts of the Clinton era, they ultimately supported large IMF rescue packages for Brazil, Uruguay, and Turkey with the familiar wall-of-money strategy. That was what the IMF was for. Years later, Mervyn King, the governor of the Bank of England, joked at a farewell dinner that I was a textbook proof of the difference between correlation and causation. “Tim was present at all the crises,” he said. “But he didn’t cause the crises. The crises caused him.” Again and again, I got to see how indulgent capital financed booms, how cracks in confidence turned boom to bust to panic, how crisis managers could help contain panics with decisiveness and overwhelming force, and how the kind of actions needed to defuse crises were inherently unpopular and fraught with risk.
Capitalism 4.0: The Birth of a New Economy in the Aftermath of Crisis by Anatole Kaletsky
bank run, banking crisis, Benoit Mandelbrot, Berlin Wall, Black Swan, bonus culture, Bretton Woods, BRICs, Carmen Reinhart, cognitive dissonance, collapse of Lehman Brothers, Corn Laws, correlation does not imply causation, credit crunch, currency manipulation / currency intervention, David Ricardo: comparative advantage, deglobalization, Deng Xiaoping, Edward Glaeser, Eugene Fama: efficient market hypothesis, eurozone crisis, experimental economics, F. W. de Klerk, failed state, Fall of the Berlin Wall, financial deregulation, financial innovation, Financial Instability Hypothesis, floating exchange rates, full employment, George Akerlof, global rebalancing, Hyman Minsky, income inequality, invisible hand, Isaac Newton, Joseph Schumpeter, Kenneth Rogoff, laissez-faire capitalism, Long Term Capital Management, mandelbrot fractal, market design, market fundamentalism, Martin Wolf, moral hazard, mortgage debt, new economy, Northern Rock, offshore financial centre, oil shock, paradox of thrift, peak oil, pets.com, Ponzi scheme, post-industrial society, price stability, profit maximization, profit motive, quantitative easing, Ralph Waldo Emerson, random walk, rent-seeking, reserve currency, rising living standards, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, short selling, South Sea Bubble, sovereign wealth fund, special drawing rights, statistical model, The Chicago School, The Great Moderation, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, Washington Consensus
The question therefore arises whether Japan is now the most plausible model for a New Normal of sluggish growth and financial paralysis in the United States, Britain, and other economies emerging from the credit crunch. Luckily, this analogy between Japan and the Western world looks increasingly far-fetched. It is certainly true that the Japanese financial system remained paralyzed for a decade as banks and borrowers survived on government life support and failed to recognize their true losses. It is true also that the Japanese economy spent twenty years almost continuously in recession. But correlation is not causation. The question that needs to be asked about the Japanese experience is whether government support for struggling banks and overindebted borrowers caused the twenty years of stagnation or whether twenty years of economic stagnation prevented a recovery for weak borrowers and banks. A similar question must be asked about a fascinating and much-quoted historic study, coauthored by Carmen Reinhart and Kenneth Rogoff, the IMF’s former chief economist, which looked at the macroeconomic effect of financial crises in dozens of countries over the past six hundred years.
David Mitchell: Back Story by David Mitchell
Although it may explain some of the murders (see Book 2). I’m always suspicious of that ‘comedy comes from pain’ reasoning. Trite magazine interviewers talk to comedians, tease a perfectly standard amount of doubt, fear and self-analysis out of them and infer therefrom that it’s this phenomenon of not-feeling-perpetually-fine that allowed them to come up with that amusing routine about towels. Well, correlation is not causation, as they say on Radio 4’s statistics programme More or Less. Everyone’s unhappy sometimes, and not everyone is funny. The interviewers may as well infer that the comedy comes from the inhalation of oxygen. Which of course it partly does. We have no evidence for any joke ever having emanated from a non-oxygen-breathing organism. At a sub-atomic level, oxygen is absolutely packed with hilarions.
23andMe, 3D printing, Affordable Care Act / Obamacare, Anne Wojcicki, Atul Gawande, augmented reality, bioinformatics, call centre, Clayton Christensen, clean water, cloud computing, computer vision, conceptual framework, connected car, correlation does not imply causation, crowdsourcing, dark matter, data acquisition, disintermediation, don't be evil, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Firefox, global village, Google Glasses, Google X / Alphabet X, Ignaz Semmelweis: hand washing, interchangeable parts, Internet of things, Isaac Newton, job automation, Joseph Schumpeter, Julian Assange, Kevin Kelly, license plate recognition, Lyft, Mark Zuckerberg, Marshall McLuhan, meta analysis, meta-analysis, microbiome, Nate Silver, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, personalized medicine, phenotype, placebo effect, RAND corporation, randomized controlled trial, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, Snapchat, social graph, speech recognition, stealth mode startup, Steve Jobs, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Turing test, Uber for X, Watson beat the top human players on Jeopardy!, X Prize
Later, a team of four highly respected data scientists wrote in Science that GFT had systematically overestimated the prevalence of flu every week since August 2011, going on to criticize “big data hubris,” the “often implicit assumption that big data are a substitute for, rather than a supplement to, traditional data collection and analysis.”17 They attacked the “algorithm dynamics” of GFT, pointing out that the forty-five search terms used were never documented, key elements such as core search terms were not provided in the publications, and the original algorithm did not undergo constant adjustment and recalibration. What’s more, while the GFT algorithm was static, the search engine itself underwent constant change—as many as six hundred revisions per year—which was not taken into account. Many other editorialists opined on the matter.13–15,18,19 Correlation rather than causation and the critical absence of context were the most prominent critique points. There was also the sampling issue as the crowdsourcing was limited to those doing searches on Google. Further, there was a major analytical problem: GFT performed so many multiple comparisons of data that they were likely to be getting spurious results. These can all be viewed as common traps when we are trying to understand the world through data.13 As Krenchel and Madsbjerg wrote in Wired, “The real big data hubris is not that we have too much confidence in a set of algorithms and methods that aren’t quite there yet.
Happy City: Transforming Our Lives Through Urban Design by Charles Montgomery
2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, agricultural Revolution, American Society of Civil Engineers: Report Card, Bernie Madoff, British Empire, Buckminster Fuller, car-free, carbon footprint, centre right, City Beautiful movement, clean water, congestion charging, correlation does not imply causation, East Village, edge city, energy security, Enrique Peñalosa, experimental subject, Frank Gehry, Google Earth, happiness index / gross national happiness, Home mortgage interest deduction, housing crisis, income inequality, income per capita, invisible hand, Jane Jacobs, license plate recognition, McMansion, means of production, megacity, Menlo Park, meta analysis, meta-analysis, mortgage tax deduction, New Urbanism, peak oil, Ponzi scheme, rent control, ride hailing / ride sharing, risk tolerance, science of happiness, Seaside, Florida, Silicon Valley, the built environment, The Death and Life of Great American Cities, the High Line, The Spirit Level, The Wealth of Nations by Adam Smith, trade route, transit-oriented development, upwardly mobile, urban planning, urban sprawl, wage slave, white flight, World Values Survey, Zipcar
And people who trust their neighbors feel a greater sense of that belonging. And that sense of belonging is influenced by social contact. And casual encounters (such as, say, the kind that might happen around a volleyball court on a Friday night) are just as important to belonging and trust as contact with family and close friends. It is hard to say which condition is lifting the others—Helliwell admits that his statistical analysis demonstrates correlation rather than causation—but what is strikingly apparent is that trust, feelings of belonging, social time, and happiness are like balloons tied together in a bouquet. They rise and fall together. This suggests that it has been a terrible mistake to design cities around the nuclear family at the expense of other ties. But it also suggests that even the high-status, deeply desired, uniquely biophilic brand of verticalism embodied by Vancouverism and McDowell’s high-rise apartment is not a panacea.
Ghettoside: A True Story of Murder in America by Jill Leovy
The tables I’ve compiled include names of victims, circumstances of deaths, and, in many cases, observations made at crime scenes and funerals and information provided by families and detectives. Over the years, in search of clarity on clearance rates, I have conducted surveys of case outcomes by calling or visiting the assigned detectives or their field supervisors and asking for updates. For years now, I have tried to penetrate the mystery of disproportionate black homicide. Correlation is not causation. I wanted to know exactly what was happening and why. I’ve sought answers in reported facts and observations, and tried to avoid pat speculation and received wisdom. Mostly, I’ve relied on what I have myself seen or heard directly from those who are close to homicide. I have made deliberate efforts to listen to the bereaved—to seek out the parents, siblings, spouses, and children of black homicide victims, whose viewpoints are under-represented in our national debates over criminal justice.
Cooked: A Natural History of Transformation by Michael Pollan
biofilm, bioinformatics, Columbian Exchange, correlation does not imply causation, dematerialisation, Drosophila, energy security, Gary Taubes, Hernando de Soto, Louis Pasteur, Mason jar, microbiome, peak oil, Ralph Waldo Emerson, Steven Pinker, women in the workforce
., “Ingestion of Lactobacillus Strain Regulates Emotional Behavior and Central GABA Receptor Expression in a Mouse via the Vagus Nerve,” Proceedings of the National Academy of Sciences 108 No. 38 : 16050–55). * It has long been recognized that people with autism and schizophrenia often suffer from gastrointestinal disorders, and some recent work suggests there may be anomalies in their microflora. It’s important to remember that correlation is not causation, and if there is causation, we don’t know which way it goes. But evidence is accumulating that certain microbes in our bodies can affect our behavior and do so for their own purposes. Toxoplasma gondii, a parasite found in more than one billion people worldwide, has been shown to inspire neurotic self-destructive behavior in rats. The protozoa’s reproductive cycle depends on infecting cats, which it does by getting them to eat the rats and mice in whose brains the parasite commonly resides.
The Age of Em: Work, Love and Life When Robots Rule the Earth by Robin Hanson
8-hour work day, artificial general intelligence, augmented reality, Berlin Wall, bitcoin, blockchain, brain emulation, business process, Clayton Christensen, cloud computing, correlation does not imply causation, demographic transition, Erik Brynjolfsson, ethereum blockchain, experimental subject, fault tolerance, financial intermediation, Flynn Effect, hindsight bias, job automation, job satisfaction, Just-in-time delivery, lone genius, Machinery of Freedom by David Friedman, market design, meta analysis, meta-analysis, Nash equilibrium, new economy, prediction markets, rent control, rent-seeking, reversible computing, risk tolerance, Silicon Valley, smart contracts, statistical model, stem cell, Thomas Malthus, trade route, Turing test, Vernor Vinge
For example, people today tend to be both happier and more productive when they have jobs, autonomy at work, health, beauty, money, marriage, religion, intelligence, extroversion, conscientiousness, agreeableness, and non-neuroticism (Myers and Diener 1995; Lykken and Tellegen 1996; Steen 1996; Nguyen et al. 2003; Barrick 2005; Roberts et al. 2007; Sutin et al. 2009; Erdogan et al. 2012; Diener 2013; Ali et al. 2013; Stutzer and Frey 2013). Of course correlation isn’t causation, and there is much we don’t understand here. Even so, the consistency of the relationship between happiness and productivity gives us much reason to hope that more productive ems may on average be happier than people today. Yes, perhaps work productivity makes people happier by raising their relative status, and by definition relative status can’t rise for everyone. But even in that case, relative status can’t fall overall either, to hurt overall happiness.
I Think You'll Find It's a Bit More Complicated Than That by Ben Goldacre
call centre, conceptual framework, correlation does not imply causation, crowdsourcing, death of newspapers, Desert Island Discs, en.wikipedia.org, experimental subject, Firefox, Flynn Effect, jimmy wales, John Snow's cholera map, Loebner Prize, meta analysis, meta-analysis, placebo effect, Simon Singh, statistical model, stem cell, the scientific method, Turing test, WikiLeaks
Systematic reviews of randomised trials are considered to be the most reliable: because they ensure that your conclusions are based on all of the information, rather than just some of it; and because randomised trials – when conducted properly – are the least vulnerable to bias, and so they are the ‘most fair tests’. After these, there are observational studies: these are much more prone to bias, and produce findings which might just reflect correlation instead of causation (‘People who choose to eat vegetables live longer’) but they are generally cheaper to do. Then there are individual case reports. And then, finally, because medical academics like to think they’re funny, right at the bottom of the hierarchy you will find something called ‘expert opinion’. In the Dartmouth study, among the press releases covering human research, only 17 per cent promoted the studies with the strongest designs, either randomised trials or meta-analyses.
accounting loophole / creative accounting, affirmative action, bank run, banking crisis, Berlin Wall, bonus culture, Branko Milanovic, BRICs, call centre, Cass Sunstein, central bank independence, collapse of Lehman Brothers, conceptual framework, corporate governance, correlation does not imply causation, Credit Default Swap, deindustrialization, demographic transition, Diane Coyle, disintermediation, Edward Glaeser, Eugene Fama: efficient market hypothesis, experimental economics, Fall of the Berlin Wall, Financial Instability Hypothesis, Francis Fukuyama: the end of history, George Akerlof, Gini coefficient, global supply chain, Gordon Gekko, greed is good, happiness index / gross national happiness, Hyman Minsky, If something cannot go on forever, it will stop, illegal immigration, income inequality, income per capita, invisible hand, Jane Jacobs, Joseph Schumpeter, Kenneth Rogoff, knowledge economy, labour market flexibility, low skilled workers, market bubble, market design, market fundamentalism, megacity, Network effects, new economy, night-watchman state, Northern Rock, oil shock, principal–agent problem, profit motive, purchasing power parity, railway mania, rising living standards, Ronald Reagan, Silicon Valley, South Sea Bubble, Steven Pinker, The Design of Experiments, The Fortune at the Bottom of the Pyramid, The Market for Lemons, The Myth of the Rational Market, The Spirit Level, transaction costs, transfer pricing, tulip mania, ultimatum game, University of East Anglia, web application, web of trust, winner-take-all economy, World Values Survey
For example, one paper reports rising happiness in forty-five of fifty-two countries for which times-series data are available (for the years 1981–2007) and links it to rising freedom and economic development.34 Another confirms that income, alongside social indicators, explains much of the difference in self-reported happiness levels within countries and between countries.35 This seems a much more credible result than the original Easterlin Paradox. But the temptation to read too much into this should be resisted. This is partly for reasons of sensible caution about the statistics. All of this work looks at statistical correlations and not at causation. Happier people might be more productive, leading to higher growth and incomes, rather than the causality running the other way. Alternatively, other factors that do cause happiness might be linked in turn to growth—such as better health or greater access to education—making the observed correlation between happiness and growth an indirect one. Moreover, there is other evidence on the relationship between economic and social measures and happiness that gives useful insights when it comes to policy.
call centre, clean water, correlation does not imply causation, Dean Kamen, double helix, edge city, germ theory of disease, Google Earth, Jane Jacobs, John Nash: game theory, John Snow's cholera map, lone genius, Louis Pasteur, megacity, mutually assured destruction, New Urbanism, nuclear winter, pattern recognition, peak oil, side project, Steven Pinker, Stewart Brand, The Death and Life of Great American Cities, the scientific method, trade route, unbiased observer, working poor
Farr thought that the single most reliable predictor of environmental contamination was elevation: the population living in the putrid fog that hung along the riverbanks were more likely to be seized by the cholera than those living in the rarefied air of, say, Hampstead. And so, after the 1849 outbreak, Farr began tabulating cholera deaths by elevation, and indeed the numbers seemed to show that higher ground was safer ground. This would prove to be a classic case of correlation being mistaken for causation: the communities at the higher elevations tended to be less densely settled than the crowded streets around the Thames, and their distance from the river made them less likely to drink its contaminated water. Higher elevations were safer, but not because they were free of miasma. They were safer because they tended to have cleaner water. Farr was not entirely opposed to Snow’s theory.
Hidden Family by Stross, Charles
He’d understand: That was half the attraction that had sparked their whirlwind affair. He probably grasped the headaches she was facing better than anyone else, Brill included. Brill was still not much more than a teenager with a sheltered upbringing. But Roland knew just how nasty things could get. If I trust him, she thought wistfully. Someone had murdered the watchman and installed the bomb in the warehouse. She’d told Roland about the place, and then … correlation does not imply causation, she told herself. In the end she compromised halfway, taking the T into town and finding a diner with a good range of exit options before switching on the phone and dialing. That way, even if someone had grabbed Roland and was actively tracing the call, they wouldn’t find her before she ended the call. It was raining, and she had a seat next to the window, watching the slug-trails of rain on the glass as her latte cooled while she tried to work up her nerve to call him.
The Elusive Quest for Growth: Economists' Adventures and Misadventures in the Tropics by William R. Easterly
Andrei Shleifer, business climate, Carmen Reinhart, central bank independence, clean water, colonial rule, correlation does not imply causation, financial repression, Gini coefficient, Hernando de Soto, income inequality, income per capita, inflation targeting, interchangeable parts, inventory management, invisible hand, Isaac Newton, Joseph Schumpeter, Kenneth Rogoff, large denomination, manufacturing employment, Network effects, New Urbanism, open economy, Productivity paradox, purchasing power parity, rent-seeking, Ronald Reagan, Silicon Valley, Simon Kuznets, The Wealth of Nations by Adam Smith, Thomas Malthus, total factor productivity, trade liberalization, urban sprawl, Watson beat the top human players on Jeopardy!, Yogi Berra, Yom Kippur War
affirmative action, Asian financial crisis, Bretton Woods, colonial rule, correlation does not imply causation, credit crunch, diversification, diversified portfolio, en.wikipedia.org, European colonialism, failed state, financial innovation, financial intermediation, Hernando de Soto, income inequality, invisible hand, M-Pesa, market fundamentalism, Mexican peso crisis / tequila crisis, microcredit, moral hazard, Ponzi scheme, rent-seeking, Ronald Reagan, sovereign wealth fund, The Chicago School, trade liberalization, transaction costs, trickle-down economics, Washington Consensus, Yom Kippur War
Despite the widespread Western belief that ‘the rich should help the poor, and the form of this help should be aid’, the reality is that aid has helped make the poor poorer, and growth slower. In Moyo’s startling words: ‘Aid has been, and continues to be, an unmitigated political, economic, and humanitarian disaster for most parts of the developing world.’ In short, it is (as Karl Kraus said of Freudianism) ‘the disease of which it pretends to be the cure’. The correlation is certainly suggestive, even if the causation may be debated. Over the past thirty years, according to Moyo, the most aid-dependent countries have exhibited an average annual growth rate of minus 0.2 per cent. Between 1970 and 1998, when aid flows to Africa were at their peak, the poverty rate in Africa actually rose from 11 per cent to a staggering 66 per cent. Why? Moyo’s crucial insight is that the receipt of concessional (non-emergency) loans and grants has much same effect in Africa as the possession of a valuable natural resource: it’s a kind of curse because it encourages corruption and conflict, while at the same time discouraging free enterprise.
What They Do With Your Money: How the Financial System Fails Us, and How to Fix It by Stephen Davis, Jon Lukomnik, David Pitt-Watson
Admiral Zheng, banking crisis, Basel III, Bernie Madoff, Black Swan, centralized clearinghouse, clean water, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, crowdsourcing, David Brooks, Dissolution of the Soviet Union, diversification, diversified portfolio, en.wikipedia.org, financial innovation, financial intermediation, Flash crash, income inequality, index fund, invisible hand, London Whale, Long Term Capital Management, moral hazard, Northern Rock, passive investing, performance metric, Ponzi scheme, principal–agent problem, rent-seeking, Ronald Coase, shareholder value, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical model, Steve Jobs, the market place, The Wealth of Nations by Adam Smith, transaction costs, Upton Sinclair, value at risk, WikiLeaks
See a May 1, 2013, debate at the American Enterprise Institute at www.aei.org/events/2013/05/01/shareholder-value-theory-myth-or-motivator/. 4. Keith Ambachtsheer, Ronald Capelle, and Hubert Lum, “The Pension Governance Deficit: Still with Us” (Social Science Research Network, 2008), http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1280907. Critics rightly caution that while the study finds correlation between fund governance and performance, causation is elusive. In other words, outperformance could help produce good governance rather than the other way around. Also, the period of four years examined by the authors is relatively short given retirement timeframes. 5. To illustrate, apply the academic projection to the real world. Let’s plug in numbers for an employee at age thirty who starts with a $50,000 a year salary and typical contributions to a 401(k).
Is God a Mathematician? by Mario Livio
Albert Einstein, Antoine Gombaud: Chevalier de Méré, Brownian motion, cellular automata, correlation coefficient, correlation does not imply causation, cosmological constant, Dava Sobel, double helix, Edmond Halley, Eratosthenes, Georg Cantor, Gerolamo Cardano, Gödel, Escher, Bach, Henri Poincaré, Isaac Newton, John von Neumann, music of the spheres, probability theory / Blaise Pascal / Pierre de Fermat, The Design of Experiments, the scientific method, traveling salesman
This is an example of selection effects—biases introduced in the results due to either the apparatus used for collecting the data or the methodology used to analyze them. Sampling presents another problem. For instance, modern opinion polls usually interview no more than a few thousand people. How can the pollsters be sure that the views expressed by members of this sample correctly represent the opinions of hundreds of millions? Another point to realize is that correlation does not necessarily imply causation. The sales of new toasters may be on the rise at the same time that audiences at concerts of classical music increase, but this does not mean that the presence of a new toaster at home enhances musical appreciation. Rather, both effects may be caused by an improvement in the economy. In spite of these important caveats, statistics have become one of the most effective instruments in modern society, literally putting the “science” into the social sciences.
Equal Is Unfair: America's Misguided Fight Against Income Inequality by Don Watkins, Yaron Brook
3D printing, Affordable Care Act / Obamacare, Apple II, barriers to entry, Berlin Wall, Bernie Madoff, blue-collar work, business process, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, collective bargaining, colonial exploitation, corporate governance, correlation does not imply causation, Credit Default Swap, crony capitalism, David Brooks, deskilling, Edward Glaeser, Elon Musk, en.wikipedia.org, financial deregulation, immigration reform, income inequality, indoor plumbing, inventory management, invisible hand, Isaac Newton, Jeff Bezos, Jony Ive, laissez-faire capitalism, Louis Pasteur, low skilled workers, means of production, minimum wage unemployment, Naomi Klein, new economy, obamacare, Peter Singer: altruism, Peter Thiel, profit motive, rent control, Ronald Reagan, Silicon Valley, Skype, statistical model, Steve Jobs, Steve Wozniak, The Spirit Level, too big to fail, trickle-down economics, Uber for X, urban renewal, War on Poverty, women in the workforce, working poor
Albert Einstein, Alfred Russel Wallace, anesthesia awareness, anthropic principle, butterfly effect, cognitive dissonance, complexity theory, conceptual framework, correlation does not imply causation, cosmological principle, discovery of DNA, false memory syndrome, Gary Taubes, invention of the wheel, Isaac Newton, laissez-faire capitalism, life extension, Murray Gell-Mann, out of africa, Richard Feynman, Richard Feynman, Search for Extraterrestrial Intelligence, Silicon Valley, Stephen Hawking, Steven Pinker, The Bell Curve by Richard Herrnstein and Charles Murray, the scientific method, Thomas Kuhn: the structure of scientific revolutions
Mothers who bottle-fed their babies were made to feel guilty. But soon researchers began to wonder whether breast-fed babies are attended to differently. Maybe nursing mothers spend more time with their babies and motherly vigilance was the cause behind the differences in IQ. As Hume taught us, the fact that two events follow each other in sequence does not mean they are connected causally. Correlation does not mean causation. 13. Coincidence In the paranormal world, coincidences are often seen as deeply significant. "Synchronicity" is invoked, as if some mysterious force were at work behind the scenes. But I see synchronicity as nothing more than a type of contingency—a conjuncture of two or more events without apparent design. When the connection is made in a manner that seems impossible according to our intuition of the laws of probability, we have a tendency to think something mysterious is at work.
Bernie Madoff, carbon footprint, cleantech, collateralized debt obligation, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, decarbonisation, Deng Xiaoping, en.wikipedia.org, energy security, energy transition, flex fuel, greed is good, Hernando de Soto, hydraulic fracturing, hydrogen economy, Indoor air pollution, Isaac Newton, James Watt: steam engine, Menlo Park, new economy, offshore financial centre, oil shale / tar sands, oil shock, peak oil, Ponzi scheme, purchasing power parity, RAND corporation, Ronald Reagan, Silicon Valley, smart grid, Stewart Brand, Thomas L Friedman, uranium enrichment, Whole Earth Catalog
As a journalist, I’m skeptical of nearly everything. When my mother told me she loved me, I doublechecked it with my dad. And now, with legions of greens, politicos, and pundits all parroting the same message about the dangers of global warming, my reflexive skepticism only increases. My skepticism about the conventional wisdom on global warming arises from two main points. First, I adhere to one of the oldest maxims in science: Correlation does not prove causation. Carbon dioxide levels in the atmosphere may be increasing, but that does not necessarily prove that the carbon dioxide is causing any warming that may be occurring. Second, models are only as good as the data going into them. All of the alarm bells now being sounded are based on atmospheric and climatic models about how temperatures in the future are expected to react, given the data fed into the models.
Flow by Mihaly Csikszentmihalyi
Albert Einstein, Bonfire of the Vanities, centralized clearinghouse, conceptual framework, correlation does not imply causation, double helix, fear of failure, Ignaz Semmelweis: hand washing, invisible hand, Isaac Newton, job satisfaction, Mahatma Gandhi, meta analysis, meta-analysis, Necker cube, pattern recognition, place-making, Ralph Waldo Emerson, the scientific method, Thomas Kuhn: the structure of scientific revolutions
It needs to be stressed again and again that what counts is the quality of experience flow provides, and that this is more important for achieving happiness than riches or fame. At the same time, it would be disingenuous to ignore the fact that successful people tend to enjoy what they do to an unusual extent. This may indicate that people who enjoy what they are doing will do a good job of it (although, as we know, correlation does not imply causation). A long time ago, Maurice Schlick (1934) pointed out how important enjoyment was in sustaining scientific creativity. In an interesting recent study, B. Eugene Griessman interviewed a potpourri of high achievers ranging from Francis H. C. Crick, the codiscoverer of the double helix, to Hank Aaron, Julie Andrews, and Ted Turner. Fifteen of these celebrities completed a questionnaire in which they rated the importance of thirty-three personal characteristics, such as creativity, competence, and breadth of knowledge, in terms of helping them achieve success.
AltaVista, barriers to entry, Black Swan, bounce rate, business intelligence, butterfly effect, call centre, Claude Shannon: information theory, complexity theory, correlation does not imply causation, en.wikipedia.org, first-price auction, information retrieval, inventory management, life extension, linear programming, megacity, Nash equilibrium, Network effects, PageRank, place-making, price mechanism, psychological pricing, random walk, Schrödinger's Cat, sealed-bid auction, search engine result page, second-price auction, second-price sealed-bid, sentiment analysis, social web, software as a service, stochastic process, telemarketer, the market place, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Vickrey auction, yield management
It is to be noted that the rule defines both the magnitude and the measure . The metrics of reach and frequency as measurements of sponsored search effects and analysis have been used in advertising for at least twenty-five years. What should be reported, however, is effective reach. That is, to be meaningful, media reach and frequency measurements must be related to advertising communication goals. Potpourri: “Correlation does not imply causation” is a common catchphrase in empirical analysis. Its meaning is that just because two variables are correlated does not mean that one causes the other. Typically, correlation is a necessary but not sufficient condition for causation. Most advertising has an objective to capture attention and maintain awareness. Advertising analysts for this reason have measured the effect of frequency based on communication goals.
Common Wealth: Economics for a Crowded Planet by Jeffrey Sachs
agricultural Revolution, air freight, back-to-the-land, British Empire, business process, carbon footprint, clean water, colonial rule, corporate social responsibility, correlation does not imply causation, demographic transition, Diane Coyle, Edward Glaeser, energy security, failed state, Gini coefficient, Haber-Bosch Process, income inequality, income per capita, intermodal, invention of agriculture, invention of the steam engine, invisible hand, Joseph Schumpeter, knowledge worker, labor-force participation, labour mobility, low skilled workers, microcredit, oil shale / tar sands, peak oil, profit maximization, profit motive, purchasing power parity, road to serfdom, Ronald Reagan, Simon Kuznets, Skype, statistical model, The Wealth of Nations by Adam Smith, Thomas Malthus, trade route, transaction costs, unemployed young men, War on Poverty, women in the workforce, working-age population
The scatter plot of 150 countries in Figure 8.2(a) shows that lower rates of under-five mortality are associated with lower rates of total fertility. The scatter plot in Figure 8.2(b) shows that lower under-five mortality is associated with a lower overall rate of population growth, suggesting that the decline in mortality is more than offset by an accompanying decline in fertility. Correlation does not prove causation, but ample experience, and more sophisticated statistical testing, does. By saving children’s lives, and reaping the benefits in lower fertility rates, societies not only save their children but also help to stabilize their populations at the same time. Figure 8.2(a): Child Mortality and Total Fertility Rates in 2005 Source: Data from World Bank (2007) Figure 8.2(b): Population Growth and Child Mortality Rates in 2005 Source: Data from World Bank (2007) Education of Girls Girls’ education has time and again been shown to be one of the decisive entry points into the demographic transition.
The Science of Fear: How the Culture of Fear Manipulates Your Brain by Daniel Gardner
Atul Gawande, availability heuristic, Black Swan, Cass Sunstein, citizen journalism, cognitive bias, cognitive dissonance, Columbine, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Brooks, Doomsday Clock, feminist movement, haute couture, hindsight bias, illegal immigration, medical residency, Mikhail Gorbachev, millennium bug, mutually assured destruction, nuclear winter, placebo effect, Ralph Nader, RAND corporation, Ronald Reagan, Stephen Hawking, Steven Levy, Steven Pinker, the scientific method, Tunguska event, uranium enrichment, Y2K, young professional
When we examine the statistics and decide that the odds of being killed in a terrorist attack are far too small to worry about, Head is doing the work. Head is our best bet for accurate results, but it has limitations. First, Head needs to be educated. We live in a world of complex information, and if Head doesn’t learn the basics of math, stats, and logic—if it doesn’t know the difference between an increaseof 5 percent and an increase of 5 percentage points, say, or that correlation does not prove causation—it can make bad mistakes. Head also works very slowly. That may not be a problem when you are reading the newspaper at the breakfast table, but it’s a little troublesome when you see a shadow move in long grass and you have to decide what to do without consulting an encyclopedia to determine the prevalence and hunting habits of lions. System One, or Gut, is unconscious thought, and its defining quality is speed.
Our Kids: The American Dream in Crisis by Robert D. Putnam
correlation does not imply causation, deindustrialization, demographic transition, desegregation, ending welfare as we know it, epigenetics, full employment, George Akerlof, helicopter parent, impulse control, income inequality, index card, low skilled workers, manufacturing employment, meta analysis, meta-analysis, mortgage tax deduction, new economy, Occupy movement, Ralph Waldo Emerson, randomized controlled trial, school choice, Socratic dialogue, The Bell Curve by Richard Herrnstein and Charles Murray, the built environment, upwardly mobile, Walter Mischel, white flight, working poor
Children born in 1990 to high school dropouts were more than four times as likely to have a parent sent to prison as were children born that same year to college-educated parents. More than half of all black children born to less educated parents in 1990 experienced parental imprisonment.57 This period of exploding incarceration is precisely the period in which single-parent families became more and more common in the less educated, lower-income stratum of the population. Correlation does not prove causation, of course, but mass incarceration has certainly removed a very large number of young fathers from poor neighborhoods, and the effects of their absence, on white and nonwhite kids alike, are known to be traumatic, leaving long-lasting scars. They certainly did in David’s life in Ohio and Joe’s life in Oregon. Paternal incarceration (independent of other facts about a child’s background, like the parents’ education and income and race) is a strong predictor of bad educational outcomes, like getting poor grades and dropping out of school.
Jennifer Morgue by Stross, Charles
call centre, correlation does not imply causation, disintermediation, dumpster diving, Etonian, haute couture, interchangeable parts, Maui Hawaii, mutually assured destruction, planetary scale, RFID, Silicon Valley, Skype, slashdot, stem cell, telepresence, traveling salesman, Turing machine
"What" "Primus, we're destiny-entangled. I can't do anything about that. You stub your toe, I hurt'; I call you names, you get pissy. But you're making a big mistake. Because, secundus, you had a weird dream. And you're jumping to the conclusion that the two are related, that whatever you dreamed about is whatever happened to me. And you know what? That ain't necessarily so. Correlation does not imply causation. Now — " she reaches over and pokes me in the chest with a fingertip " — you seem a little upset over whatever it was you dreamed about. And I think you ought to think very hard before you ask the next question, because you can choose to ask whether there was any connection between your weird dream and my night out — or you can just tell yourself you ate too many cheese canapes before bed and it was all in your head, and you can walk away from it.
Albert Einstein, Asian financial crisis, Barry Marshall: ulcers, Berlin Wall, Big bang: deregulation of the City of London, California gold rush, complexity theory, computer age, constrained optimization, corporate governance, corporate social responsibility, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, Donald Trump, double entry bookkeeping, double helix, Edward Lloyd's coffeehouse, equity premium, Ernest Rutherford, European colonialism, experimental economics, Exxon Valdez, failed state, financial innovation, Francis Fukuyama: the end of history, George Akerlof, George Gilder, greed is good, haute couture, illegal immigration, income inequality, invention of the telephone, invention of the wheel, invisible hand, John Nash: game theory, John von Neumann, Kevin Kelly, knowledge economy, labour market flexibility, late capitalism, Long Term Capital Management, loss aversion, Mahatma Gandhi, market bubble, market clearing, market fundamentalism, means of production, Menlo Park, Mikhail Gorbachev, money: store of value / unit of account / medium of exchange, moral hazard, Naomi Klein, Nash equilibrium, new economy, oil shale / tar sands, oil shock, pets.com, popular electronics, price discrimination, price mechanism, prisoner's dilemma, profit maximization, purchasing power parity, QWERTY keyboard, Ralph Nader, RAND corporation, random walk, rent-seeking, risk tolerance, road to serfdom, Ronald Coase, Ronald Reagan, second-price auction, shareholder value, Silicon Valley, Simon Kuznets, South Sea Bubble, Steve Jobs, telemarketer, The Chicago School, The Death and Life of Great American Cities, The Market for Lemons, The Nature of the Firm, The Predators' Ball, The Wealth of Nations by Adam Smith, Thorstein Veblen, total factor productivity, transaction costs, tulip mania, urban decay, Washington Consensus, women in the workforce, yield curve, yield management
24 Openness-productive countries have fewer restrictions on trade with other countries. 25 Population growth is lower in productive economies. 26 Property rights are more secure in rich states. 27 Religion-from the standpoint of earthly productivity, it is better to live in a society whose traditions are Christian, and among Christians, it is better to live in a predominantly Protestant tradition than in a mainly Catholic one. 28 Tolerance-more people in rich states answer yes to questions like "Should people be allowed to live as they choose?" 29 Correlation does not imply causation. Average height is greater in rich states. Are tall people more productive than short people? Or does higher productivity make people taller? I doubt if either of these things is true. The most likely explanation is that higher standards ofliving, which result from higher productivity, lead to better nutrition. In turn, better nutrition leads to greater adult height and still higher productivity.
Inside the Nudge Unit: How Small Changes Can Make a Big Difference by David Halpern
Affordable Care Act / Obamacare, availability heuristic, carbon footprint, Cass Sunstein, centre right, choice architecture, cognitive dissonance, collaborative consumption, correlation does not imply causation, Daniel Kahneman / Amos Tversky, endowment effect, happiness index / gross national happiness, hindsight bias, illegal immigration, job satisfaction, Kickstarter, libertarian paternalism, market design, meta analysis, meta-analysis, Milgram experiment, nudge unit, peer-to-peer lending, pension reform, presumed consent, quantitative easing, randomized controlled trial, Richard Feynman, Richard Thaler, Ronald Reagan, Rory Sutherland, Simon Kuznets, skunkworks, the built environment, theory of mind, traffic fines, World Values Survey
Even a cursory glance at the relationship between levels of subjective well-being and income strongly suggests that money does buy at least some happiness.9 The correlation between the two, at national level, is around 0.8 – or about as strong a relationship as is found in the social sciences. A similar shaped curvilinear relationship is found within countries, with the rich consistently reporting greater life satisfaction than the poor. Of course, correlation does not imply causation. It is likely that at least some of this relationship is mediated by other factors, such as better healthcare and education in richer countries or places. It is even plausible that some of the differences in income are partly driven by well-being, rather than the other way around, at least within some populations. It is much less likely that individual level differences in outlook, or positive psychology, can explain national differences in GDP, though some have suggested it.10 There’s more to material and environmental factors than income, of course.
Airbnb, artificial general intelligence, asset allocation, Atul Gawande, augmented reality, back-to-the-land, Bernie Madoff, Bertrand Russell: In Praise of Idleness, Black Swan, blue-collar work, Buckminster Fuller, business process, Cal Newport, call centre, Checklist Manifesto, cognitive bias, cognitive dissonance, Colonization of Mars, Columbine, correlation does not imply causation, David Brooks, David Graeber, diversification, diversified portfolio, Donald Trump, effective altruism, Elon Musk, fault tolerance, fear of failure, Firefox, follow your passion, future of work, Google X / Alphabet X, Howard Zinn, Hugh Fearnley-Whittingstall, Jeff Bezos, job satisfaction, Johann Wolfgang von Goethe, Kevin Kelly, Kickstarter, Lao Tzu, life extension, Mahatma Gandhi, Mark Zuckerberg, Mason jar, Menlo Park, Mikhail Gorbachev, Nicholas Carr, optical character recognition, PageRank, passive income, pattern recognition, Paul Graham, Peter H. Diamandis: Planetary Resources, Peter Singer: altruism, Peter Thiel, phenotype, post scarcity, premature optimization, QWERTY keyboard, Ralph Waldo Emerson, Ray Kurzweil, recommendation engine, rent-seeking, Richard Feynman, Richard Feynman, risk tolerance, Ronald Reagan, sharing economy, side project, Silicon Valley, skunkworks, Skype, Snapchat, social graph, software as a service, software is eating the world, stem cell, Stephen Hawking, Steve Jobs, Stewart Brand, superintelligent machines, Tesla Model S, The Wisdom of Crowds, Thomas L Friedman, Wall-E, Washington Consensus, Whole Earth Catalog, Y Combinator
I see, hear, feel, and know that the purpose of my life is to inspire and guide the transformation of humanity on and off the Earth.” Peter’s breathing is similar to some of Wim Hof’s exercises (page 41), which I now do in a cold shower (state “priming” per Tony Robbins, page 210), right after my morning meditation. As for the flossing-longevity connection, Peter is the first to admit this might be correlation instead of causation: People anal retentive enough to floss regularly probably have other habits that directly contribute to longer life. Pre-Bed Routines Before bed, Peter always reviews his three “wins of the day.” This is analogous to the 5-Minute Journal p.m. review that I do (page 146). On Getting out of Funks TIM: “To get out of that 2-day funk [after one of his early startups failed], what does the self-talk look like?
Trick or Treatment: The Undeniable Facts About Alternative Medicine by Edzard Ernst, Simon Singh
Barry Marshall: ulcers, Berlin Wall, correlation does not imply causation, false memory syndrome, Florence Nightingale: pie chart, germ theory of disease, John Snow's cholera map, Louis Pasteur, meta analysis, meta-analysis, placebo effect, profit motive, randomized controlled trial, Ronald Reagan, Simon Singh, The Design of Experiments, the scientific method
The central problem is that we are tempted to assume that two events that happen one after the other must be connected. If recovery from illness takes place after taking some homeopathic pills, then isn’t it obvious that the homeopathic pills caused the recovery? If there is a correlation between two events, then isn’t it common sense that one event caused the other? The answer is ‘No’. We can see why a correlation should not be confused with causation if we look at a neat example invented by Bobby Henderson, author of The Gospel of the Flying Spaghetti Monster. He spotted a very interesting correlation between the increase in global temperature over the last two centuries and the decline in the number of pirates. If correlation is synonymous with cause and effect, then he speculated that the decline in pirates is causing global warming.
Adapt: Why Success Always Starts With Failure by Tim Harford
Andrew Wiles, banking crisis, Basel III, Berlin Wall, Bernie Madoff, Black Swan, car-free, carbon footprint, Cass Sunstein, charter city, Clayton Christensen, clean water, cloud computing, cognitive dissonance, complexity theory, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, Dava Sobel, Deep Water Horizon, Deng Xiaoping, double entry bookkeeping, Edmond Halley, en.wikipedia.org, Erik Brynjolfsson, experimental subject, Fall of the Berlin Wall, Fermat's Last Theorem, Firefox, food miles, Gerolamo Cardano, global supply chain, Isaac Newton, Jane Jacobs, Jarndyce and Jarndyce, Jarndyce and Jarndyce, John Harrison: Longitude, knowledge worker, loose coupling, Martin Wolf, Menlo Park, Mikhail Gorbachev, mutually assured destruction, Netflix Prize, New Urbanism, Nick Leeson, PageRank, Piper Alpha, profit motive, Richard Florida, Richard Thaler, rolodex, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, South China Sea, special economic zone, spectrum auction, Steve Jobs, supply-chain management, the market place, The Wisdom of Crowds, too big to fail, trade route, Tyler Cowen: Great Stagnation, web application, X Prize
Sometimes there is no choice but to perform an experiment yourself. Even with better data, the truth is not always apparent. For example, Lind had speculated that scurvy was connected with beer, because he noticed that scurvy often struck when a ship’s supply of beer ran out. But this was coincidence: both were the result of a long voyage, but scurvy has nothing to do with a deficiency of beer. Correlation is a treacherous guide to causation. There is, naturally, an ethical question over all this. Ten of Lind’s twelve scurvy sufferers saw their illnesses deteriorate as they took salt water, sulphuric acid and various other substances that proved to be useless as cures for scurvy. When we really have no idea what the right treatment is, there is little downside here: with the possible exception of the pair taking sulphuric acid, the ten sick sailors would have been no worse off without Lind on board.
Guns, germs, and steel: the fates of human societies by Jared M. Diamond
affirmative action, Atahualpa, British Empire, California gold rush, correlation does not imply causation, cuban missile crisis, discovery of the americas, European colonialism, Francisco Pizarro, Hernando de Soto, invention of movable type, invention of the wheel, invention of writing, James Watt: steam engine, Maui Hawaii, QWERTY keyboard, the scientific method, trade route
Endless Money: The Moral Hazards of Socialism by William Baker, Addison Wiggin
Andy Kessler, asset allocation, backtesting, bank run, banking crisis, Berlin Wall, Bernie Madoff, Black Swan, Branko Milanovic, Bretton Woods, BRICs, business climate, capital asset pricing model, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, crony capitalism, cuban missile crisis, currency manipulation / currency intervention, debt deflation, Elliott wave, en.wikipedia.org, Fall of the Berlin Wall, feminist movement, fiat currency, fixed income, floating exchange rates, Fractional reserve banking, full employment, German hyperinflation, housing crisis, income inequality, index fund, inflation targeting, Joseph Schumpeter, laissez-faire capitalism, land reform, liquidity trap, Long Term Capital Management, McMansion, moral hazard, mortgage tax deduction, naked short selling, offshore financial centre, Ponzi scheme, price stability, pushing on a string, quantitative easing, RAND corporation, rent control, reserve currency, riskless arbitrage, Ronald Reagan, school vouchers, seigniorage, short selling, Silicon Valley, six sigma, statistical arbitrage, statistical model, Steve Jobs, The Great Moderation, the scientific method, time value of money, too big to fail, upwardly mobile, War on Poverty, Yogi Berra, young professional
This firm’s researchers, who are tasked to make money rather than to win Nobel Prizes, would be unlikely to bet heavily on a correlation that cannot explain nearly half of a model’s outcome. Foolishly, those running the Fed would not hesitate to do so even though the wealth of America and the world is put at great risk. Moreover, the first thing statistics students are taught is that correlation does not necessarily mean causation. The stock market may rise when old NFL franchises win the Super Bowl and fall otherwise; it is inexplicable why this is so, and clearly there is no cause and effect. After having solved the riddle of the Great Depression, to leave the back door open by saying that this is so except for the experience of the 1920s and 1930s seems odd. And it is especially suspect now that in a world of freely floating currencies the freezing up of credit markets caused the stock market to crash in a mere subset of months within 2008.
The Origins of Political Order: From Prehuman Times to the French Revolution by Francis Fukuyama
Admiral Zheng, agricultural Revolution, Andrei Shleifer, Asian financial crisis, Ayatollah Khomeini, barriers to entry, Berlin Wall, blood diamonds, California gold rush, cognitive dissonance, colonial rule, conceptual framework, correlation does not imply causation, currency manipulation / currency intervention, demographic transition, Deng Xiaoping, double entry bookkeeping, equal pay for equal work, European colonialism, failed state, Fall of the Berlin Wall, Francis Fukuyama: the end of history, Francisco Pizarro, Hernando de Soto, hiring and firing, invention of agriculture, invention of the printing press, Khyber Pass, labour market flexibility, land reform, land tenure, means of production, offshore financial centre, out of africa, Peace of Westphalia, principal–agent problem, RAND corporation, rent-seeking, Scramble for Africa, spice trade, Stephen Hawking, Steven Pinker, the scientific method, The Wealth of Nations by Adam Smith, Thomas L Friedman, Thomas Malthus, trade route, transaction costs, Washington Consensus
Putting the two together makes possible mental models—that is, general statements about causation (“it gets warm because the sun shines”; “society forces girls into stereotyped gender roles”). All human beings engage in the construction of abstract mental models; our ability to theorize in this fashion gives us huge survival advantages. Despite the warnings of philosophers like David Hume and countless professors in first-year statistics classes that correlation does not imply causation, human beings are constantly observing correlations between events in the world around them and inferring causation from them. By not stepping on the snake or eating the root that killed your cousin last week, you avoid being subject to the same fate, and you can quickly communicate that rule to your offspring. The ability to create mental models and to attribute causality to invisible abstractions is in turn the basis for the emergence of religion.
Albert Einstein, correlation coefficient, correlation does not imply causation, Gary Taubes, Indoor air pollution, meta analysis, meta-analysis, phenotype, placebo effect, randomized controlled trial, Robert Gordon, the scientific method, Upton Sinclair
The idea that fat might lead to cancer was first aired at the McGovern committee hearings in 1976, when Gio Gori, director of the National Cancer Institute (NCI), testified that men and women in Japan had very low rates of breast and colon cancer and that those rates rose quickly upon emigrating to the United States. Gori showed charts demonstrating the parallel rising lines of fat consumption and cancer rates. “Now I want to emphasize that this is a very strong correlation, but that correlation does not mean causation,” he said. “I don’t think anybody can go out today, and say that food causes cancer.” He urged more research. However, the Senate committee, in its enthusiasm to solve as many of the nations’ health problems as possible, overlooked those reservations, and implied in its report that a low-fat diet could help reduce cancer risk. Cancer thus became the second “killer disease” that the Senate pinned on the back of fat consumption.
The confusion by Neal Stephenson
correlation does not imply causation, dark matter, Fellow of the Royal Society, Filipino sailors, invisible hand, Isaac Newton, out of africa, Socratic dialogue, South China Sea, spice trade, urban planning, web of trust
And so only since war broke out has any progress been made here.” “No. I meant, do you know why they remodelled?” “From the looks of it I should say it was de Maintenon.” “De Maintenon?!” De Gex’s reaction told Eliza that her answer had been emphatically wrong. “Yes,” she said, “she came along in 1685, did she not? Which is when this remodel got under way…and the subject matter of the painting is so markedly Maintenon-esque.” “Correlation is not causation,” de Gex said. “They had to remodel, because of a disastrous Incident that took place in that year.” And then De Gex seemed to remember that they were in a hurry, and once again began striding toward the library. Eliza stomped along beside, and a little behind him. “You do know what happened here—?” he continued, and glanced back at her. “Something grievously embarrassing—so embarrassing that no one will tell me what it was.”