andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2037 knowledge-graph by maker-knowledge-mining
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Introduction: James Robins, Tyler VanderWeele, and Richard Gill write : Neyman introduced a formal mathematical theory of counterfactual causation that now has become standard language in many quantitative disciplines, but not in physics. We use results on causal interaction and interference between treatments (derived under the Neyman theory) to give a simple new proof of a well-known result in quantum physics, namely, Bellís inequality. Now the predictions of quantum mechanics and the results of experiment both violate Bell’s inequality. In the remainder of the talk, we review the implications for a counterfactual theory of causation. Assuming with Einstein that faster than light (supraluminal) communication is not possible, one can view the Neyman theory of counterfactuals as falsified by experiment. . . . Is it safe for a quantitative discipline to rely on a counterfactual approach to causation, when our best confirmed physical theory falsifies their existence? I haven’t seen the talk
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1 James Robins, Tyler VanderWeele, and Richard Gill write : Neyman introduced a formal mathematical theory of counterfactual causation that now has become standard language in many quantitative disciplines, but not in physics. [sent-1, score-1.135]
2 We use results on causal interaction and interference between treatments (derived under the Neyman theory) to give a simple new proof of a well-known result in quantum physics, namely, Bellís inequality. [sent-2, score-0.786]
3 Now the predictions of quantum mechanics and the results of experiment both violate Bell’s inequality. [sent-3, score-0.889]
4 In the remainder of the talk, we review the implications for a counterfactual theory of causation. [sent-4, score-0.686]
5 Assuming with Einstein that faster than light (supraluminal) communication is not possible, one can view the Neyman theory of counterfactuals as falsified by experiment. [sent-5, score-0.511]
6 Is it safe for a quantitative discipline to rely on a counterfactual approach to causation, when our best confirmed physical theory falsifies their existence? [sent-9, score-1.101]
7 The problem is not special to counterfactual analysis; it’s with conditional probability more generally. [sent-12, score-0.497]
8 If you recall your college physics, you’ll realize that the results of the two-slit experiment violate the laws of joint probability, as we discussed a few years ago here and here . [sent-13, score-0.571]
9 Given that classical probability theory (that is, the equation P(A&B;)=B(A|B)P(B)) does not fit quantum reality, it makes sense to me that the Neyman-Rubin model of causation, which in practice is always applied with probabilistic models, will not work in the quantum realm. [sent-14, score-1.224]
10 If one tries to imagine applying potential-outcomes notation to the two-slit experiment, you’ll see that it just won’t work. [sent-15, score-0.229]
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Introduction: James Robins, Tyler VanderWeele, and Richard Gill write : Neyman introduced a formal mathematical theory of counterfactual causation that now has become standard language in many quantitative disciplines, but not in physics. We use results on causal interaction and interference between treatments (derived under the Neyman theory) to give a simple new proof of a well-known result in quantum physics, namely, Bellís inequality. Now the predictions of quantum mechanics and the results of experiment both violate Bell’s inequality. In the remainder of the talk, we review the implications for a counterfactual theory of causation. Assuming with Einstein that faster than light (supraluminal) communication is not possible, one can view the Neyman theory of counterfactuals as falsified by experiment. . . . Is it safe for a quantitative discipline to rely on a counterfactual approach to causation, when our best confirmed physical theory falsifies their existence? I haven’t seen the talk
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Introduction: I read this article by Rivka Galchen on quantum computing. Much of the article was about an eccentric scientist in his fifties named David Deutch. I’m sure the guy is brilliant but I wasn’t particularly interested in his not particularly interesting life story (apparently he’s thin and lives in Oxford). There was a brief description of quantum computing itself, which reminds me of the discussion we had a couple years ago under the heading, The laws of conditional probability are false (and the update here ). I don’t have anything new to say here; I’d just never heard of quantum computing before and it seemed relevant to our discussion. The uncertainty inherent in quantum computing seems closely related to Jouni’s idea of fully Bayesian computing , that uncertainty should be inherent in the computational structure rather than tacked on at the end. P.S. No, I’m not working on July 4th! This post is two months old, we just have a long waiting list of blog entries.
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Introduction: A tall thin young man came to my office today to talk about one of my current pet topics: stories and social science. I brought up Tom Wolfe and his goal of compressing an entire city into a single novel, and how this reminded me of the psychologists Kahneman and Tversky’s concept of “the law of small numbers,” the idea that we expect any small sample to replicate all the properties of the larger population that it represents. Strictly speaking, the law of small numbers is impossible—any small sample necessarily has its own unique features—but this is even more true if we consider network properties. The average American knows about 700 people (depending on how you define “know”) and this defines a social network over the population. Now suppose you look at a few hundred people and all their connections. This mini-network will almost necessarily look much much sparser than the national network, as we’re removing the connections to the people not in the sample. Now consider how
Introduction: Erin Jonaitis points us to this article by Christopher Ferguson and Moritz Heene, who write: Publication bias remains a controversial issue in psychological science. . . . that the field often constructs arguments to block the publication and interpretation of null results and that null results may be further extinguished through questionable researcher practices. Given that science is dependent on the process of falsification, we argue that these problems reduce psychological science’s capability to have a proper mechanism for theory falsification, thus resulting in the promulgation of numerous “undead” theories that are ideologically popular but have little basis in fact. They mention the infamous Daryl Bem article. It is pretty much only because Bem’s claims are (presumably) false that they got published in a major research journal. Had the claims been true—that is, had Bem run identical experiments, analyzed his data more carefully and objectively, and reported that the r
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