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Propensity Score Analysis: Statistical Methods and Applications

New edition forthcoming

Shenyang Guo and Mark W. Fraser
Publisher: Sage
Copyright: 2009
ISBN-13: 978-1-4129-5356-6
Pages: 370; hardcover

Comment from the Stata technical group

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 observation data will find this book invaluable. This book is the first to provide step-by-step instructions for using Stata to estimate treatments 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 authors discuss the issues involved in using observational data. The authors have consolidated the notation and theory found in the propensity-score literature and have carried this notation thr oughout the book. 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 the early formulation of the Neyman–Rubin counterfactual framework to a discussion of current methods and research issues. These ideas are discussed at theoretical and applied levels. The authors formally describe the assumptions underlying the analyses in a way that provides insight into the issues related to applying these techniques to practical problems.

Propensity-score analysis has it 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 is bringing these two viewpoints together in a unified, balanced manner.

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.

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