andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-879 knowledge-graph by maker-knowledge-mining
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Introduction: Judea Pearl is starting an (online) Journal of Causal Inference. The first issue is planned for Fall 2011 and the website is now open for submissions. Here’s the background (from Pearl): Existing discipline-specific journals tend to bury causal analysis in the language and methods of traditional statistical methodologies, creating the inaccurate impression that causal questions can be handled by routine methods of regression, simultaneous equations or logical implications, and glossing over the special ingredients needed for causal analysis. In contrast, Journal of Causal Inference highlights both the uniqueness and interdisciplinary nature of causal research. In addition to significant original research articles, Journal of Causal Inference also welcomes: 1) Submissions that synthesize and assess cross-disciplinary methodological research 2) Submissions that discuss the history of the causal inference field and its philosophical underpinnings 3) Unsolicited short communi
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1 The first issue is planned for Fall 2011 and the website is now open for submissions. [sent-2, score-0.276]
2 In contrast, Journal of Causal Inference highlights both the uniqueness and interdisciplinary nature of causal research. [sent-4, score-0.676]
3 I don’t have any submissions myself right now, but if I were going to write something for the journal, perhaps I would send in something like this article on experimental reasoning in social science. [sent-7, score-0.37]
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Introduction: Judea Pearl is starting an (online) Journal of Causal Inference. The first issue is planned for Fall 2011 and the website is now open for submissions. Here’s the background (from Pearl): Existing discipline-specific journals tend to bury causal analysis in the language and methods of traditional statistical methodologies, creating the inaccurate impression that causal questions can be handled by routine methods of regression, simultaneous equations or logical implications, and glossing over the special ingredients needed for causal analysis. In contrast, Journal of Causal Inference highlights both the uniqueness and interdisciplinary nature of causal research. In addition to significant original research articles, Journal of Causal Inference also welcomes: 1) Submissions that synthesize and assess cross-disciplinary methodological research 2) Submissions that discuss the history of the causal inference field and its philosophical underpinnings 3) Unsolicited short communi
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Introduction: Pearl reports that his Journal of Causal Inference has just posted its first issue , which contains a mix of theoretical and applied papers. Pearl writes that they welcome submissions on all aspects of causal inference.
Introduction: Consider two broad classes of inferential questions : 1. Forward causal inference . What might happen if we do X? What are the effects of smoking on health, the effects of schooling on knowledge, the effect of campaigns on election outcomes, and so forth? 2. Reverse causal inference . What causes Y? Why do more attractive people earn more money? Why do many poor people vote for Republicans and rich people vote for Democrats? Why did the economy collapse? When statisticians and econometricians write about causal inference, they focus on forward causal questions. Rubin always told us: Never ask Why? Only ask What if? And, from the econ perspective, causation is typically framed in terms of manipulations: if x had changed by 1, how much would y be expected to change, holding all else constant? But reverse causal questions are important too. They’re a natural way to think (consider the importance of the word “Why”) and are arguably more important than forward questions.
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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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.
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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 .
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Introduction: In the annals of hack literature, it is sometimes said that if you aim to write best-selling crap, all you’ll end up with is crap. To truly produce best-selling crap, you have to have a conviction, perhaps misplaced, that your writing has integrity. Whether or not this is a good generalization about writing, I have seen an analogous phenomenon in statistics: If you try to do nothing but model the data, you can be in for a wild and unpleasant ride: real data always seem to have one more twist beyond our ability to model (von Neumann’s elephant’s trunk notwithstanding). But if you model the underlying process, sometimes your model can fit surprisingly well as well as inviting openings for future research progress.
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Introduction: Tyler Cowen pointed to an article by business-school professor Luigi Zingales about meritocracy. I’d expect a b-school prof to support the idea of meritocracy, and Zingales does not disappoint. But he says a bunch of other things that to me represent a confused conflation of ideas. Here’s Zingales: America became known as a land of opportunity—a place whose capitalist system benefited the hardworking and the virtuous [emphasis added]. In a word, it was a meritocracy. That’s interesting—and revealing. Here’s what I get when I look up “meritocracy” in the dictionary : 1 : a system in which the talented are chosen and moved ahead on the basis of their achievement 2 : leadership selected on the basis of intellectual criteria Nothing here about “hardworking” or “virtuous.” In a meritocracy, you can be as hardworking as John Kruk or as virtuous as Kobe Bryant and you’ll still get ahead—if you have the talent and achievement. Throwing in “hardworking” and “virtuous”
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Introduction: I had a submission a couple years ago that was rejected by a journal. One of the reviewers began with the following snotty aside: In this manuscript Gelman and Shalizi (there’s no anonymity here; this thing has been floating around the web for some time) . . . Actually, we posted it on the same day we submitted it to the journal. But double-blindness allowed the reviewer to act as if we had done something wrong! And, even if it had been “floating around the web for some time,” that’s not necessarily a bad thing. Perhaps it just meant that the article had previously been rejected by a bad-attitude reviewer!
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