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Comment by jwtadvice | Hacker News Reader

I read through about 10 pages of the first book ("without models"). It struck me as very nearly identical to current statistics practice. It clearly differentiated itself as discussing counterfactuals (the data needed to actually determine causality) but I could not find the section of the book that described how counterfactual data can be inferred from missing data (without it being "turtles all the way down").

Does anyone in this area have a succinct way to explain how counterfactual data can be inferred by these techniques - and how traditional statistics practice is not able to perform this inference?

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Part II describes what you are asking for.

I wish abbreviations like inverse probability weighting and marginal structural models were expanded in the second book. It's annoying to have to look up "IP weighting" only to discover "oh, it's IPW, god damn you [authors] to hell".

MSMs are interesting. Now I remember why Robins' name stuck in my head. The book shows all the math explicitly, which is nice, and it delves into causal inference for time-to-event data, which is also nice.

Now I'm curious whether they look at piecewise constant survival models for time-varying coefficients. It's mentioned, but I didn't read enough to see if it's treated in detail. If it is, everyone who does A/B testing should read the book, because this is one of those "little details" from biostatistics that becomes super important at big retailers (like, say, Amazon, where the principal economist at the time pointed it out to me).

Answered my own question -- Part III of this book is what I am waiting for. I was going to send the authors a note, which seems silly, but my name is in an awful lot of standard textbooks just because I sent in corrections or notes, and for some reason I find that satisfying. Like I made some sort of a difference to some students somewhere.

Sorry if I missed something - where do you see a part III?

It's mentioned at the end of Part III, in chapter 17, on survival models.

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