In a previous post, I began following the developments in Miguel Hernán and James Robins’ soon to be published book, Causal Inference. There I gave an overview of the first topics they cover, namely potential outcomes, causal effects, and randomization. In this post I’ll continue, with some personal notes on the remaining parts of Chapter 2 of their book, on conditional randomization, standardization, and inverse probability weighting.
Potential outcomes, counterfactuals, causal effects, and randomization
Next week I’ll be attending the third UK Causal Inference Meeting, in Bristol. Causal inference has seen a tremendous amount of methodological development over the last 20 years, and recently a number of books have been published on the topic. In advance of attending the conference, I’ve been reading through a draft of the excellent book by Miguel Hernán (who is giving a pre-conference course) and James Robins on ‘Causal Inference’ (freely downloadable here). So far I’ve found the book highly readable and intuitive. As I’m working through it, I thought I’d write some posts giving overviews of some of the material covered, which I personally find useful to help cement the ideas in my own mind, and possibly might be of use to others.