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Comment by ced | Hacker News Reader

I was looking for a review, and found Gelman's recommendation of

Causal Inference for Statistics, Social, and Biomedical Sciences

and mention of this book: http://www.hsph.harvard.edu/miguel-hernan/causal-inference-b...

Does anyone have a comment on those? I've read Pearl's two earlier books, and found the one on causality quite hard to navigate. The basic ideas are cool, but it's hard to connect the more advanced theorems with anything I could actually implement.

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I too found Pearl's book hard to navigate on first attempt. Do not let that stop you! After a hiatus, I stumbled upon this blog post [1], which explained the core ideas in Pearl's framework beautifully in a simple language. My advice is to persist, fill any holes in fundamentals (mostly basic probability), and persist. After working out the examples in the blog post on paper and contrasting it to other ideas out there (potential outcome framework), it became quite clear what Pearl was trying to articulate.

Pearl is also an enthusiastic speaker. You can search for his talks online at various venues (Stanford, Microsoft Research, etc.) to learn more.

[1] http://www.michaelnielsen.org/ddi/if-correlation-doesnt-impl...

Gelman's own book (with Jennifer Hill) also has some good, practical techniques on causal inference. [0]

Morgan and Winship's Counterfactuals and Causal Inference: Methods and Principles for Social Research [1] is also really good. Be sure to get the second edition; it's much better than the first.

[0] http://www.amazon.com/Analysis-Regression-Multilevel-Hierarc...

[1] http://www.amazon.com/Counterfactuals-Causal-Inference-Princ...

I studied the Hernan/Robins book for a course on causal inference, and I love it. But frankly it's a pretty niche topic, and so for the non-statisticians here on HN who are trying to get better at statistics, just keep in mind that there are so many other topics you probably want to tackle first. Many of the concerns addressed in the Hernan/Robins book probably wouldn't even make sense to you if you didn't have a firm statistical background already.

Gelman/Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models or Angrist/Pischke's Mastering 'Metrics embed ideas and techniques from causal inference into the broader context of regression modeling which makes these books more immediately useful. Those would probably be my two recommendations for non-statisticians.

I'm a very big fan of Miguel Hernan's work, and have found him fairly approachable - he's made a decent stab at taking some of the more opaque bits of epidemiology methods and making them more clear.

They're both great books, but they make heavier use of potential outcome notation and focus less on graph-theoretic formulations than Pearl's work.

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