Counterfactuals and Causal Inference is a very practical book that discusses the different approaches to identify causal effects (in non-experimental and experimental data) at a very abstract level. Depending on the reader this may be a good or not so good thing. I had to expend substantial effort to work through the text and I fear that even though I understand directed acyclical graphs I have not developed any intuition in their application that would help me in my applied modelling. Often, the text remains at a too abstract level.
What the text is missing is an even more practical guide with more concrete applied problems and their solutions. Yet, the text is good. It’s not a handbook for a quick how to do it. It’s not a textbook for undergraduates. It’s a critical survey of the state of the art of statistical approaches for the identification of causal effects. It’s a valuable reminder that the regression approach is no magic bullet.
That being said, the text raises the important question of identification and alerted me that some effects that we estimate and report may not be the effects that we would like them to be. I guess I will have to be even more careful when I interpret regressions in the future.
Addendum: I have read the first edition that I had for already some years sitting on my to-read shelf. I just discovered that there is a 2nd, revised edition available.