更改

===Goodreads书评===

===Goodreads书评===

“I read this book because I was so impressed with the importance of causal inferencing from data, as Pearl has developed it, in his more popular book, "The Book of Why". But that book didn't present enough detail for me to actually apply causal analysis to my own data. I hoped that a self-styled "Primer" would fill that gap.

 

For a scientist, Pearl is an outstandingly good writer. Even his journal articles are readable! His popular book (The Book of Why: The New Science of Cause and Effect) was quite enjoyable and this textbook is concise and clear. I read this book at the same time as I was reading Causal Inference: What If and found that, for the concepts that are covered in both, Pearl's presentation was more clear.

 

The book has only four chapters. Chapter 1 gives an overview of causal inference and a brief overview of important background from probability theory. Chapter 2 lays out the graphical approach to structural causal models and introduces d-separation. Chapter 3 shows how to connect the graphical models to causal questions, with the central focus being back-door and front-door corrections, including a section on inverse probability weighting and linear models/regression. Chapter 4 expands the computational methods to a wider array of counterfactual questions, ending with extensive examples and explicit formulas for direct and indirect effects under mediation.

 

The greatest weakness of "The Book of Why" is that the examples are not worked out clearly. After reading it, you are convinced that there is something to this causal inference thing, but you have no intuition as to how to solve even the simplest problem involving confounders or colliders. In that sense, the current book is a worthy sequel. If you follow the equations and exercises (not particularly difficult), you will know how to solve simple causal inference problems.

 

That raises the question of who this book is intended for. I found the book to be challenging, but not bewildering or unnecessarily complex. I should note that I have a Ph.D. in physics and have worked for several years in something like "data science." In theory the book is intended for undergraduates who are studying "elementary statistics." I guess they would get something out of it, but my guess is that a beginning student would have difficulty appreciating what the relevance of these methods is without familiarity with how things are treated in the absence of them. After all, the math itself is rather simple, comfortably at the level of a precocious undergrad. What makes the subject challenging is understanding how to translate back and forth between assumptions about reality, graphs/equations and numerical estimates.

 

 

One last point is that there is an ongoing struggle within the causal inference community between Pearl's approach and the Neyman's potential outcomes approach. You'll see it obliquely referenced here, in Hernan's book (from the other side), in sparring journal articles and--of course--on academic Twitter. I kind of wish the argument would be less partisan and more philosophical but I guess even scientists are human beings. To get a sense of what the argument is about, I'd read https://ftp.cs.ucla.edu/pub/stat_ser/...。”——Michael