This is a chapter by chapter review of Causality (2nd ed.) by Judea Pearl (UCLA, blog). Like my previous review, the intention is not to summarize but to help readers determine whether or not they should read the book (and if they do, what parts to read). Reading the review is in no way a substitute for reading the book.

I'll state my basic impression of the book up front, with detailed comments after the chapter discussions: this book is monumentally important to anyone interested in procuring knowledge (especially causal knowledge) from statistical data, but it is a heavily technical book primarily suitable for experts. The mathematics involved is not particularly difficult, but its presentation requires dedicated reading and clarity of thought. Only the epilogue, this lecture, is suitable for the general audience, and that will be the highest value portion for most readers of LW.

1. Introduction to Probabilities, Graphs, and Causal Models

While the descriptions are complete, this chapter may be more useful as a refresher than as an introduction. The three sections are detailed in inverse proportion to the expected reader's familiarity.

For the reader who's seen probability calculus before, Pearl's description of it in 12 pages is short, sweet, and complete. For the reader that hasn't seen it, that's just enough space to list the definitions and give a few examples. Compare Eliezer's explanation of Bayes' Rule (almost 50 pages) to Pearl's (around 2).

The section on graphs moves a little less quickly, but even so don't be afraid to find an online tutorial on d-separation if Pearl's explanation is too fast. For some reason, he does not mention here that section 11.1.2 (p 335-337 in my copy) is a gentler introduction to d-separation. [edit] His blog also linked to this presentation, which is an even gentler introduction to graphs, causal networks, and d-separation.

The section on causal models is the most detailed, as it will be new to most readers, and closely follows the section on graphs. Pearl uses an example to demonstrate the use of counterfactuals, which is a potent first glance at the usefulness of causal models.

He also draws an i

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