andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1624 knowledge-graph by maker-knowledge-mining
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Introduction: Judea Pearl writes: Can you post the announcement below on your blog? And, by all means, if you find heresy in my interview with Ron Wasserstein, feel free to criticize it with your readers. I responded that I’m not religious, so he’ll have to look for someone else if he’s looking for findings of heresy. I did, however, want to share his announcement: The American Statistical Association has announced a new Prize , “Causality in Statistics Education”, aimed to encourage the teaching of basic causal inference in introductory statistics courses. The motivations for the prize are discussed in an interview I [Pearl] gave to Ron Wasserstein. I hope readers of this list will participate, either by innovating new tools for teaching causation or by nominating candidates who deserve the prize. And speaking about education, Bryant and I [Pearl] have revised our survey of econometrics textbooks, and would love to hear your suggestions on how to restore causal inference to e
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1 Judea Pearl writes: Can you post the announcement below on your blog? [sent-1, score-0.226]
2 And, by all means, if you find heresy in my interview with Ron Wasserstein, feel free to criticize it with your readers. [sent-2, score-0.351]
3 I responded that I’m not religious, so he’ll have to look for someone else if he’s looking for findings of heresy. [sent-3, score-0.217]
4 I did, however, want to share his announcement: The American Statistical Association has announced a new Prize , “Causality in Statistics Education”, aimed to encourage the teaching of basic causal inference in introductory statistics courses. [sent-4, score-1.064]
5 The motivations for the prize are discussed in an interview I [Pearl] gave to Ron Wasserstein. [sent-5, score-0.654]
6 I hope readers of this list will participate, either by innovating new tools for teaching causation or by nominating candidates who deserve the prize. [sent-6, score-0.867]
7 And speaking about education, Bryant and I [Pearl] have revised our survey of econometrics textbooks, and would love to hear your suggestions on how to restore causal inference to econometrics education. [sent-7, score-1.454]
8 [I'm confused on that last point; I thought that causality was central to econometrics; see, for example, Angrist and Pischke's book . [sent-8, score-0.386]
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Introduction: Judea Pearl writes: Can you post the announcement below on your blog? And, by all means, if you find heresy in my interview with Ron Wasserstein, feel free to criticize it with your readers. I responded that I’m not religious, so he’ll have to look for someone else if he’s looking for findings of heresy. I did, however, want to share his announcement: The American Statistical Association has announced a new Prize , “Causality in Statistics Education”, aimed to encourage the teaching of basic causal inference in introductory statistics courses. The motivations for the prize are discussed in an interview I [Pearl] gave to Ron Wasserstein. I hope readers of this list will participate, either by innovating new tools for teaching causation or by nominating candidates who deserve the prize. And speaking about education, Bryant and I [Pearl] have revised our survey of econometrics textbooks, and would love to hear your suggestions on how to restore causal inference to e
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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: This material should be familiar to many of you but could be helpful to newcomers. Pearl writes: ALL causal conclusions in nonexperimental settings must be based on untested, judgmental assumptions that investigators are prepared to defend on scientific grounds. . . . To understand what the world should be like for a given procedure to work is of no lesser scientific value than seeking evidence for how the world works . . . Assumptions are self-destructive in their honesty. The more explicit the assumption, the more criticism it invites . . . causal diagrams invite the harshest criticism because they make assumptions more explicit and more transparent than other representation schemes. As regular readers know (for example, search this blog for “Pearl”), I have not got much out of the causal-diagrams approach myself, but in general I think that when there are multiple, mathematically equivalent methods of getting the same answer, we tend to go with the framework we are used
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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
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: Judea Pearl writes: Can you post the announcement below on your blog? And, by all means, if you find heresy in my interview with Ron Wasserstein, feel free to criticize it with your readers. I responded that I’m not religious, so he’ll have to look for someone else if he’s looking for findings of heresy. I did, however, want to share his announcement: The American Statistical Association has announced a new Prize , “Causality in Statistics Education”, aimed to encourage the teaching of basic causal inference in introductory statistics courses. The motivations for the prize are discussed in an interview I [Pearl] gave to Ron Wasserstein. I hope readers of this list will participate, either by innovating new tools for teaching causation or by nominating candidates who deserve the prize. And speaking about education, Bryant and I [Pearl] have revised our survey of econometrics textbooks, and would love to hear your suggestions on how to restore causal inference to e
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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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Introduction: Jeff points us to this news article by Asher Mullard: Bayer halts nearly two-thirds of its target-validation projects because in-house experimental findings fail to match up with published literature claims, finds a first-of-a-kind analysis on data irreproducibility. An unspoken industry rule alleges that at least 50% of published studies from academic laboratories cannot be repeated in an industrial setting, wrote venture capitalist Bruce Booth in a recent blog post. A first-of-a-kind analysis of Bayer’s internal efforts to validate ‘new drug target’ claims now not only supports this view but suggests that 50% may be an underestimate; the company’s in-house experimental data do not match literature claims in 65% of target-validation projects, leading to project discontinuation. . . . Khusru Asadullah, Head of Target Discovery at Bayer, and his colleagues looked back at 67 target-validation projects, covering the majority of Bayer’s work in oncology, women’s health and cardiov
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Introduction: Those of you who know what I’m talking about, know what I’m talking about.
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Introduction: A few days after “ Dramatic study shows participants are affected by psychological phenomena from the future ,” (see here ) the British Psychological Society follows up with “ Can psychology help combat pseudoscience? .” Somehow I’m reminded of that bit of financial advice which says, if you want to save some money, your best investment is to pay off your credit card bills.
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Introduction: Sometimes when I submit an article to a journal it is accepted right away or with minor alterations. But many of my favorite articles were rejected or had to go through an exhausting series of revisions. For example, this influential article had a very hostile referee and we had to seriously push the journal editor to accept it. This one was rejected by one or two journals before finally appearing with discussion. This paper was rejected by the American Political Science Review with no chance of revision and we had to publish it in the British Journal of Political Science, which was a bit odd given that the article was 100% about American politics. And when I submitted this instant classic (actually at the invitation of the editor), the referees found it to be trivial, and the editor did me the favor of publishing it but only by officially labeling it as a discussion of another article that appeared in the same issue. Some of my most influential papers were accepted right
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