The second edition of Propensity Score Analysis by Shenyang Guo and Mark W. Fraser is an excellent book on estimating treatment effects from observational data. Researchers and graduate students interested in the analysis of observational data will find this book invaluable. This book is the first to provide step-by-step instructions for using Stata to estimate treatment effects by propensity-score analysis. In addition, this book also covers Heckman’s sample-selection estimator, nearest-neighbor matching estimators, propensity-score matching, and propensity-score nonparametric regression estimators.

After providing a useful history of these techniques, the book includes a discussion on the counterfactual framework for estimating treatment effects involved in using observational data. The authors have consolidated the notation and theory found in the propensity-score literature, and this notation is carried throughout the text. This book introduces the philosophy and methods of matching estimation in an approachable manner, even for those unfamiliar with the subject.

Readers embark on an interesting journey from a discussion on the early formulation of the Neyman–Rubin counterfactual framework to one on current methods and research issues. These ideas are discussed at theoretical and applied levels. The authors of the book formally describe the assumptions underlying the analyses to enlighten readers about the issues related to applying these techniques to practical problems.

Propensity-score analysis has roots in both economics and statistics. Economists will find the discussion of Heckman’s sample-selection estimator familiar, and statisticians will find the Rosenbaum and Rubin counterfactual framework familiar. The authors do a great job of bringing these two viewpoints together in a unified, balanced manner.

New to the second edition are sections on multivalued treatments, generalized propensity-score estimators, and enhanced sections on propensity-score weighting estimators. Most of the examples in this book use Stata, and many of the estimators discussed in this new edition are implemented in theteffects command available in Stata 13.

While this book makes an excellent text for a graduate-level course on the analysis of observational data, it is readily accessible to researchers using observational data to estimate treatment effects. This book should be a required read for students in economics and statistics.