31–40 of 43 posts
However, the account of traditional statistics given in this article is misleading. Randomisation is a major part of traditional stats, and it is inherently a causal hypothesis: breaking the links between unobserved covariates and treatment regimes.
An important alternate contemporary causal inference framework by Rubin has origins in a 1923 thesis...
...but the content of Pearl's approach seems superior; if you ignore the academic spats.
> “but given my own background I could not but wonder how much farther Pearl would have gotten had he had the training I did as a philosopher.” The review had some good call outs but given the above quote and a few surrounding criticisms, this review is more pretentious than anything.
I like the review, but the criticism of distinguishing causality from counterfactual reasoning feels weak to me. Do we actually care about the counterfactual reasoning most? Of course. But establishing causality as its own thing before counterfactuals is necessary in the way that Pearl has structured his math. And even the grammar of human languages enforces this separation of concepts. Do you need causality to even…
I am not sure how to define "cause" without introducing counterfactuals. "This happened because I did that" seems to imply "Had I not done that, this would not have happened" -- a counterfactual.
But RCT fails -- gives a nonsense result -- when the hypothesis under test is incoherent. This wouldn't matter, except that RCT is routinely used in such circumstances, and the results treated as gospel by people in positions of authority.
Consider: Outcome X may have six causes A-F. RCT tests B, and finds that varying B only affects one in six cases. With infinitely many trials, the relationship resolves, but with one trial the difference is indistinguishable from noise.
Substitute a medical symptom for X, and a medical treatment that addresses one of six causes, for B. After one RCT, B is "shown" to be ineffective.
The problem is not B. The problem is that X is ill-defined. X could be a mental illness, or tumors in a given organ. How often do we read "anti-depressants shown ineffective"? The only way we have to distinguish one variety of depression from the next is which treatment works.
The problem is not limited to medicine.
This presents the RCT criterion uncritically, albeit citing a case where it would be hard to apply. But RCT fails -- gives a nonsense result -- when the hypothesis under test is incoherent. This wouldn't matter, except that RCT is routinely used in such circumstances, and the results treated as gospel by people in positions of authority. Consider: Outcome X may have six causes A-F. RCT tests B, and finds that varying…
I mostly agree with Pearl. However, the account of traditional statistics given in this article is misleading. Randomisation is a major part of traditional stats, and it is inherently a causal hypothesis: breaking the links between unobserved covariates and treatment regimes. An important alternate contemporary causal inference framework by Rubin has origins in a 1923 thesis... ...but the content of Pearl's approach…
Yes, randomization is central to classical statistics, but no, it is not inherently causal. Drawing a random sample from a bivariate distribution (X,Y) is key to doing a lot (though not all) of classical statistical inference (think of estimating slopes in regression), but the randomization does not imply anything about the causal relationship between X and Y. When you speak of randomization in the context of "treatment regimes," you are thinking about randomized controlled trials, which the piece does analyze explicitly, in some detail. So in this sense the account given in the essay is not misleading.
I’m 30 pages into the book. It hasn’t grabbed me yet. Worth finishing?
I mostly agree with Pearl. However, the account of traditional statistics given in this article is misleading. Randomisation is a major part of traditional stats, and it is inherently a causal hypothesis: breaking the links between unobserved covariates and treatment regimes. An important alternate contemporary causal inference framework by Rubin has origins in a 1923 thesis... ...but the content of Pearl's approach…
>> Randomisation is a major part of traditional stats, and it is inherently a causal hypothesis Yes, randomization is central to classical statistics, but no, it is not inherently causal. Drawing a random sample from a bivariate distribution (X,Y) is key to doing a lot (though not all) of classical statistical inference (think of estimating slopes in regression), but the randomization does not imply anything about th…
Anyway, the section you're pointing to agrees with me. It just happens to be overlooked when they summarise...
This presents the RCT criterion uncritically, albeit citing a case where it would be hard to apply. But RCT fails -- gives a nonsense result -- when the hypothesis under test is incoherent. This wouldn't matter, except that RCT is routinely used in such circumstances, and the results treated as gospel by people in positions of authority. Consider: Outcome X may have six causes A-F. RCT tests B, and finds that varying…
A nice point. But this is not a criticism of the RCT so much as a criticism of how its results are used or interpreted.