andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1802 knowledge-graph by maker-knowledge-mining
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Introduction: A couple people pointed me to a recent article , “Detecting Causality in Complex Ecosystems,” by fisheries researchers George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael Fogarty, and Stephan Munch. I don’t know anything about ecology research but I could imagine this method being useful in that field. I can’t see the approach doing much in political science, where I think their stated goal of “identifying causal networks” is typically irrelevant. That said, if you replace the word “causality” by “predictability” everywhere in the paper, it starts to make a lot more sense. As they write, they are working within “a framework that uses predictability as opposed to correlation to identify causation between time-series variables.” Setting causation aside, predictability is an important topic in itself. The search for patterns of predictability in complex structures may motivate causal hypotheses that can be studied more directly, using more traditional statis
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1 A couple people pointed me to a recent article , “Detecting Causality in Complex Ecosystems,” by fisheries researchers George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael Fogarty, and Stephan Munch. [sent-1, score-0.307]
2 I don’t know anything about ecology research but I could imagine this method being useful in that field. [sent-2, score-0.264]
3 I can’t see the approach doing much in political science, where I think their stated goal of “identifying causal networks” is typically irrelevant. [sent-3, score-0.441]
4 That said, if you replace the word “causality” by “predictability” everywhere in the paper, it starts to make a lot more sense. [sent-4, score-0.415]
5 As they write, they are working within “a framework that uses predictability as opposed to correlation to identify causation between time-series variables. [sent-5, score-1.357]
6 ” Setting causation aside, predictability is an important topic in itself. [sent-6, score-0.908]
7 The search for patterns of predictability in complex structures may motivate causal hypotheses that can be studied more directly, using more traditional statistical designs such as experiments and observational studies. [sent-7, score-1.95]
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same-blog 1 0.99999982 1802 andrew gelman stats-2013-04-14-Detecting predictability in complex ecosystems
Introduction: A couple people pointed me to a recent article , “Detecting Causality in Complex Ecosystems,” by fisheries researchers George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael Fogarty, and Stephan Munch. I don’t know anything about ecology research but I could imagine this method being useful in that field. I can’t see the approach doing much in political science, where I think their stated goal of “identifying causal networks” is typically irrelevant. That said, if you replace the word “causality” by “predictability” everywhere in the paper, it starts to make a lot more sense. As they write, they are working within “a framework that uses predictability as opposed to correlation to identify causation between time-series variables.” Setting causation aside, predictability is an important topic in itself. The search for patterns of predictability in complex structures may motivate causal hypotheses that can be studied more directly, using more traditional statis
2 0.14114991 1709 andrew gelman stats-2013-02-06-The fractal nature of scientific revolutions
Introduction: Phil Earnhardt writes: I stumbled across your blog entry after googling on those terms. If I could comment on the closed entry [We had to shut off comments on old blog entries for reasons of spam --- ed.], I’d note: scientific revolutions are fractal; they’re also chaotic in their dynamics. Predictability when a particular scientific revolution will take hold—or be rejected—is problematic. I find myself wishing that Chaos Theory had been established when Kuhn wrote his essay.
3 0.1266963 1778 andrew gelman stats-2013-03-27-My talk at the University of Michigan today 4pm
Introduction: Causality and Statistical Learning Andrew Gelman, Statistics and Political Science, Columbia University Wed 27 Mar, 4pm, Betty Ford Auditorium, Ford School of Public Policy Causal inference is central to the social and biomedical sciences. There are unresolved debates about the meaning of causality and the methods that should be used to measure it. As a statistician, I am trained to say that randomized experiments are a gold standard, yet I have spent almost all my applied career analyzing observational data. In this talk we shall consider various approaches to causal reasoning from the perspective of an applied statistician who recognizes the importance of causal identification yet must learn from available information. Two relevant papers are here and here .
4 0.12308913 950 andrew gelman stats-2011-10-10-“Causality is almost always in doubt”
Introduction: Dave Backus writes: We macroeconomists are thrilled with the Nobel prize for Sargent and Sims. But on causality: they spent more time showing how hard it was to identify causality than showing how to do it. And that’s a fair assessment of our field [economics]: causality is almost always in doubt. More here . If I were in a snarky mood, I’d say something like, Causality is always in doubt in economics . . . unless you’re talking about abortion and crime, in which case you can be absolutely certain. But I’m in a good mood right now so I won’t say that. Instead I’ll just remark that, as a statistician, I’m positively thrilled that somebody named “Sims” received a major award.
Introduction: Elias Bareinboim asked what I thought about his comment on selection bias in which he referred to a paper by himself and Judea Pearl, “Controlling Selection Bias in Causal Inference.” I replied that I have no problem with what he wrote, but that from my perspective I find it easier to conceptualize such problems in terms of multilevel models. I elaborated on that point in a recent post , “Hierarchical modeling as a framework for extrapolation,” which I think was read by only a few people (I say this because it received only two comments). I don’t think Bareinboim objected to anything I wrote, but like me he is comfortable working within his own framework. He wrote the following to me: In some sense, “not ad hoc” could mean logically consistent. In other words, if one agrees with the assumptions encoded in the model, one must also agree with the conclusions entailed by these assumptions. I am not aware of any other way of doing mathematics. As it turns out, to get causa
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same-blog 1 0.95808446 1802 andrew gelman stats-2013-04-14-Detecting predictability in complex ecosystems
Introduction: A couple people pointed me to a recent article , “Detecting Causality in Complex Ecosystems,” by fisheries researchers George Sugihara, Robert May, Hao Ye, Chih-hao Hsieh, Ethan Deyle, Michael Fogarty, and Stephan Munch. I don’t know anything about ecology research but I could imagine this method being useful in that field. I can’t see the approach doing much in political science, where I think their stated goal of “identifying causal networks” is typically irrelevant. That said, if you replace the word “causality” by “predictability” everywhere in the paper, it starts to make a lot more sense. As they write, they are working within “a framework that uses predictability as opposed to correlation to identify causation between time-series variables.” Setting causation aside, predictability is an important topic in itself. The search for patterns of predictability in complex structures may motivate causal hypotheses that can be studied more directly, using more traditional statis
2 0.79530412 340 andrew gelman stats-2010-10-13-Randomized experiments, non-randomized experiments, and observational studies
Introduction: In the spirit of Dehejia and Wahba: Three Conditions under Which Experiments and Observational Studies Produce Comparable Causal Estimates: New Findings from Within-Study Comparisons , by Cook, Shadish, and Wong. Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments, by Shadish, Clark, and Steiner. I just talk about causal inference. These people do it. The second link above is particularly interesting because it includes discussions by some causal inference heavyweights. WWJD and all that.
3 0.78510165 785 andrew gelman stats-2011-07-02-Experimental reasoning in social science
Introduction: As a statistician, I was trained to think of randomized experimentation as representing the gold standard of knowledge in the social sciences, and, despite having seen occasional arguments to the contrary, I still hold that view, expressed pithily by Box, Hunter, and Hunter (1978) that “To find out what happens when you change something, it is necessary to change it.” At the same time, in my capacity as a social scientist, I’ve published many applied research papers, almost none of which have used experimental data. In the present article, I’ll address the following questions: 1. Why do I agree with the consensus characterization of randomized experimentation as a gold standard? 2. Given point 1 above, why does almost all my research use observational data? In confronting these issues, we must consider some general issues in the strategy of social science research. We also take from the psychology methods literature a more nuanced perspective that considers several differen
4 0.77102745 1996 andrew gelman stats-2013-08-24-All inference is about generalizing from sample to population
Introduction: Jeff Walker writes: Your blog has skirted around the value of observational studies and chided folks for using causal language when they only have associations but I sense that you ultimately find value in these associations. I would love for you to expand this thought in a blog. Specifically: Does a measured association “suggest” a causal relationship? Are measured associations a good and efficient way to narrow the field of things that should be studied? Of all the things we should pursue, should we start with the stuff that has some largish measured association? Certainly many associations are not directly causal but due to joint association. Similarly, there must be many variables that are directly causally associated ( A -> B) but the effect, measured as an association, is masked by confounders. So if we took the “measured associations are worthwhile” approach, we’d never or rarely find the masked effects. But I’d also like to know if one is more likely to find a large causal
5 0.75353783 2286 andrew gelman stats-2014-04-08-Understanding Simpson’s paradox using a graph
Introduction: Joshua Vogelstein pointed me to this post by Michael Nielsen on how to teach Simpson’s paradox. I don’t know if Nielsen (and others) are aware that people have developed some snappy graphical methods for displaying Simpson’s paradox (and, more generally, aggregation issues). We do some this in our Red State Blue State book, but before that was the BK plot, named by Howard Wainer after a 2001 paper by Stuart Baker and Barnett Kramer, although in apparently appeared earlier in a 1987 paper by Jeon, Chung, and Bae, and doubtless was made by various other people before then. Here’s Wainer’s graphical explication from 2002 (adapted from Baker and Kramer’s 2001 paper): Here’s the version from our 2007 article (with Boris Shor, Joe Bafumi, and David Park): But I recommend Wainer’s article (linked to above) as the first thing to read on the topic of presenting aggregation paradoxes in a clear and grabby way. P.S. Robert Long writes in: I noticed your post ab
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1 0.96577412 1589 andrew gelman stats-2012-11-25-Life as a blogger: the emails just get weirder and weirder
Introduction: In the email the other day, subject line “Casting blogger, writer, journalist to host cable series”: Hi there Andrew, I’m casting a male journalist, writer, blogger, documentary filmmaker or comedian with a certain type personality for a television pilot along with production company, Pipeline39. See below: A certain type of character – no cockiness, no ego, a person who is smart, savvy, dry humor, but someone who isn’t imposing, who can infiltrate these organizations. This person will be hosting his own show and covering alternative lifestyles and secret societies around the world. If you’re interested in hearing more or would like to be considered for this project, please email me a photo and a bio of yourself, along with contact information. I’ll respond to you ASAP. I’m looking forward to hearing from you. *** Casting Producer (646) ***.**** ***@gmail.com I was with them until I got to the “no ego” part. . . . Also, I don’t think I could infiltrate any org
Introduction: Tapen Sinha writes: Living in Mexico, I have been witness to many strange (and beautiful) things. Perhaps the strangest happened during the first outbreak of A(H1N1) in Mexico City. We had our university closed, football (soccer) was played in empty stadiums (or should it be stadia) because the government feared a spread of the virus. The Metro was operating and so were the private/public buses and taxis. Since the university was closed, we took the opportunity to collect data on facemask use in the public transport systems. It was a simple (but potentially deadly!) exercise in first hand statistical data collection that we teach our students (Although I must admit that I did not dare sending my research assistant to collect data – what if she contracted the virus?). I believe it was a unique experiment never to be repeated. The paper appeared in the journal Health Policy. From the abstract: At the height of the influenza epidemic in Mexico City in the spring of 2009, the f
3 0.94219518 413 andrew gelman stats-2010-11-14-Statistics of food consumption
Introduction: Visual Economics shows statistics on average food consumption in America: My brief feedback is that water is confounded with these results. They should have subtracted water content from the weight of all dietary items, as it inflates the proportion of milk, vegetable and fruit items that contain more water. They did that for soda (which is represented as sugar/corn syrup), amplifying the inconsistency. Time Magazine had a beautiful gallery that visualizes diets around the world in a more appealing way.
4 0.93688983 1856 andrew gelman stats-2013-05-14-GPstuff: Bayesian Modeling with Gaussian Processes
Introduction: I think it’s part of my duty as a blogger to intersperse, along with the steady flow of jokes, rants, and literary criticism, some material that will actually be useful to you. So here goes. Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, and Aki Vehtari write : The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for Bayesian inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods. We can actually now fit Gaussian processes in Stan . But for big problems (or even moderately-sized problems), full Bayes can be slow. GPstuff uses EP, which is faster. At some point we’d like to implement EP in Stan. (Right now we’re working with Dave Blei to implement VB.) GPstuff really works. I saw Aki use it to fit a nonparametric version of the Bangladesh well-switching example in ARM. He was sitting in his office and just whip
5 0.93599498 1677 andrew gelman stats-2013-01-16-Greenland is one tough town
Introduction: Americans (including me) don’t know much about other countries. Jeff Lax sent me to this blog post by Myrddin pointing out that Belgium has a higher murder rate than the rest of Western Europe. I have no particular take on this, but it’s a good reminder that other countries differ from each other. Here in the U.S., we tend to think all western European countries are the same, all eastern European countries are the same, etc. In reality, Sweden is not Finland . P.S. According to the Wiki , Greenland is one tough town. I guess there’s nothing much to do out there but watch satellite TV, chew the blubber, and kill people.
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