Propensity Score Analysis

Fundamentals and Developments

Edited by Wei Pan and Haiyan Bai

Hardcovere-bookprint + e-book
April 7, 2015
ISBN 9781462519491
Price: $56.00
402 Pages
Size: 6⅛" x 9¼"
April 7, 2015
Price: $56.00
402 Pages
print + e-book
Hardcover + e-Book (PDF) ?
Price: $112.00 $61.60
402 Pages

This book is designed to help researchers better design and analyze observational data from quasi-experimental studies and improve the validity of research on causal claims. It provides clear guidance on the use of different propensity score analysis (PSA) methods, from the fundamentals to complex, cutting-edge techniques. Experts in the field introduce underlying concepts and current issues and review relevant software programs for PSA. The book addresses the steps in propensity score estimation, including the use of generalized boosted models, how to identify which matching methods work best with specific types of data, and the evaluation of balance results on key background covariates after matching. Also covered are applications of PSA with complex data, working with missing data, controlling for unobserved confounding, and the extension of PSA to prognostic score analysis for causal inference. User-friendly features include statistical program codes and application examples. Data and software code for the examples are available at the companion website (

“Pan and Bai have assembled a comprehensive volume on all aspects of propensity score methods. Both the user and the statistician will find something to like in this book. I recommend it.”

—William R. Shadish, PhD, Distinguished Professor of Psychology, University of California, Merced

“This book effectively synthesizes general principles of PSA with recent developments regarding complex issues such as estimation techniques, covariate balance, weighting, complex datasets, and sensitivity analysis. The discussion of statistical software and examples of computer code are helpful additions. This book will be useful to graduate students and applied researchers who are interested in learning about PSA for the first time or who have some knowledge and would like to learn about issues and recent developments. I recommend it as a textbook for graduate-level courses in methods of causal inference or as a reference for researchers in the social and biomedical sciences.”

—Suzanne E. Graham, EdD, Department of Education, University of New Hampshire

“There is no question that this book will serve as an excellent resource for those who want to add PSA to their repertoire of analytical methods. The chapters provide sufficient materials and examples to help both newbies and seasoned analysts deal with the methodological and practical challenges of applying PSA in research work.”

—Xitao Fan, PhD, Chair Professor and Dean, Faculty of Education, University of Macau, China

“This book is a go-to guide for designing and analyzing observational data. The editors have produced a brilliant work that addresses both methodological and practical issues in propensity score analysis. A 'must read' for all biostatisticians as well as applied researchers in the social, behavioral, and health sciences.”

—Ding-Geng (Din) Chen, PhD, School of Nursing and Department of Biostatistics and Computational Biology, University of Rochester Medical Center

Table of Contents

I. Fundamentals of Propensity Score Analysis

1. Propensity Score Analysis: Concepts and Issues, Wei Pan & Haiyan Bai

2. Overview of Implementing Propensity Score Analysis in Statistical Software, Megan Schuler

II. Propensity Score Estimation, Matching, and Covariate Balance

3. Propensity Score Estimation with Boosted Regression, Lane F. Burgette, Daniel F. McCaffrey, & Beth Ann Griffin

4. Methodological Considerations in Implementing Propensity Score Matching, Haiyan Bai

5. Evaluating Covariate Balance, Cassandra W. Pattanayak

III. Weighting Schemes and Other Strategies for Outcome Analysis after Matching

6. Propensity Score Adjustment Methods, M. H. Clark

7. Propensity Score Analysis with Matching Weights, Liang Li, Tom H. Greene, & Brian C. Sauer

8. Robust Outcome Analysis for Propensity-Matched Designs, Scott F. Kosten, Joseph W. McKean, & Bradley E. Huitema

IV. Propensity Score Analysis on Complex Data

9. Latent Growth Modeling of Longitudinal Data with Propensity-Score-Matched Groups, Walter L. Leite

10. Propensity Score Matching on Multilevel Data, Qiu Wang

11. Propensity Score Analysis with Complex Survey Samples, Debbie L. Hahs-Vaughn

V. Sensitivity Analysis and Extensions Related to Propensity Score Analysis

12. Missing Data in Propensity Scores, Robin Mitra

13. Unobserved Confounding in Propensity Score Analysis, Rolf H. H. Groenwold & Olaf H. Klungel

14. Propensity-Score-Based Sensitivity Analysis, Lingling Li, Changyu Shen, & Xiaochun Li

15. Prognostic Scores in Clustered Settings, Ben Kelcey & Christopher M. Swoboda

Author Index

Subject Index

About the Editors


About the Editors

Wei Pan, PhD, is Associate Professor and Biostatistician in the School of Nursing at Duke University. His research interests include causal inference (confounding, propensity score analysis, and resampling), advanced modeling (multilevel, structural, and mediation and moderation), meta-analysis, and their applications in the social, behavioral, and health sciences. Dr. Pan has published over 50 articles in refereed journals, as well as other publications, and has served on the editorial boards of several journals.He is the recipient of several awards for excellence in research, teaching, and service.

Haiyan Bai, PhD, is Associate Professor of Quantitative Research Methodology at the University of Central Florida. Her interests include resampling methods, propensity score analysis, research design, measurement and evaluation, and the applications of statistical methods in the educational and behavioral sciences. She has published a book on resampling methods as well as numerous articles in refereed journals, and has served on the editorial boards of several journals. Dr. Bai is a Fellow of the Academy for Teaching, Learning, and Leadership and a Faculty Fellow at the University of Central Florida, where she has been the recipient of several awards for excellence in research and teaching.


Haiyan Bai, PhD, Department of Educational and Human Sciences, University of Central Florida, Orlando, Florida

Lane F. Burgette, PhD, RAND Corporation, Arlington, Virginia

M. H. Clark, PhD, Department of Educational and Human Sciences, University of Central Florida, Orlando, Florida

Tom H. Greene, PhD, Division of Epidemiology, University of Utah, Salt Lake City, Utah

Beth Ann Griffin, PhD, RAND Corporation, Arlington, Virginia

Rolf H. H. Groenwold, MD, PhD, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands

Debbie L. Hahs-Vaughn, PhD, Department of Educational and Human Sciences, University of Central Florida, Orlando, Florida

Bradley E. Huitema, PhD, Department of Psychology, Western Michigan University, Kalamazoo, Michigan

Ben Kelcey, PhD, School of Education, University of Cincinnati, Cincinnati, Ohio

Olaf H. Klungel, PharmD, PhD, Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht University, Utrecht, The Netherlands

Scott F. Kosten, PhD, inVentiv Health Clinical, Coopersville, Michigan

Walter L. Leite, PhD, Research and Evaluation Methodology Program, University of Florida, Gainesville, Florida

Liang Li, PhD, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas

Lingling Li, PhD, Department of Population Medicine, Harvard Medical School, and Harvard Pilgrim Health Care Institute, Boston, Massachusetts

Xiaochun Li, PhD, Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana

Daniel F. McCaffrey, PhD, Educational Testing Service, Princeton, New Jersey

Joseph W. McKean, PhD, Department of Statistics, Western Michigan University, Kalamazoo, Michigan

Robin Mitra, PhD, Mathematical Sciences, University of Southampton, Southampton, United Kingdom

Wei Pan, PhD, School of Nursing, Duke University, Durham, North Carolina

Cassandra W. Pattanayak, PhD, Quantitative Analysis Institute, Wellesley College, Wellesley, Massachusetts

Brian C. Sauer, PhD, Division of Epidemiology, University of Utah, Salt Lake City, Utah

Megan Schuler, PhD, The Methodology Center, The Pennsylvania State University, State College, Pennsylvania

Changyu Shen, PhD, Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana

Christopher M. Swoboda, PhD, School of Education, University of Cincinnati, Cincinnati, Ohio

Qiu Wang, PhD, Department of Higher Education, Syracuse University, Syracuse, New York


Applied researchers and graduate students in psychology, education, management, sociology, and public health.

Course Use

Serves as a core text for a graduate seminar in Propensity Score Analysis, or as a supplement in such courses as Advanced Quantitative Methods, Research Design, and Causal Modeling.