1st Edition
Handbook of Matching and Weighting Adjustments for Causal Inference
An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete.
When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.
Part I: Conceptual issues
- Overview of methods for adjustment and applications in the social and behavioral sciences: The role of study design
Ting-Hsuan Chang and Elizabeth A. Stuart - Propensity score
Paul R. Rosenbaum - Generalization and Transportability
Elizabeth Tipton and Erin Hartman - Optimization techniques in multivariate matching
Paul R. Rosenbaum and José R. Zubizarreta - Optimal Full matching
Mark M. Fredrickson and Ben Hansen - Fine balance and its variations in modern optimal matching
Samuel D. Pimentel - Matching with instrumental variables
Mike Baiocchi and Hyunseung Kang - Covariate Adjustment in Regression Discontinuity Designs
Matias D. Cattaneo, Luke Keele, Rocío Titiunik - Risk Set Matching
Bo Lu and Robert A. Greevy, Jr. - Matching with Multilevel Data
Samuel D. Pimentel and Luke Keele - Effect Modification in Observational Studies
Kwonsang Lee and Jesse Y. Hsu - Optimal Nonbipartite Matching
Robert A. Greevy, Jr. and Bo Lu - Matching Methods for Large Observational Studies
Ruoqi Yu - Overlap Weighting
Fan Li - Covariate Balancing Propensity Score
Kosuke Imai and Yang Ning - Balancing Weights for Causal Inference
Eric R. Cohn, Eli Ben-Michael, Avi Feller, and José R. Zubizarreta - Assessing Principal Causal Effects Using Principal Score Methods
Alessandra Mattei, Laura Forastiere, Fabrizia Mealli - Incremental Causal Effects: An Introduction and Review
Matteo Bonvini, Alec McClean, Zach Branson and Edward H. Kennedy - Weighting Estimators for Causal Mediation
Donna L. Coffman, Megan S. Schuler, Trang Q. Nguyen, and Daniel F. McCaffrey - Machine Learning for Causal Inference
Jennifer Hill, George Perrett and Vincent Dorie - Treatment Heterogeneity with Survival Outcomes
Yizhe Xu, Nikolaos Ignatiadis, Erik Sverdrup, Scott Fleming, Stefan Wager, Nigam Shah - Why Machine Learning Cannot Ignore Maximum Likelihood Estimation
Mark J. van der Laan and Sherri Rose - Bayesian Propensity Score methods and Related Approaches for Confounding Adjustment
- How to Be a Good Critic of an Observational Study
Dylan S. Small - Sensitivity Analysis
C.B. Fogarty - Evidence Factors
Bikram Karmakar
Part II: Matching
Part III: Weighting
Part IV: Machine Learning Adjustments
Joseph Antonelli
Part V: Beyond Adjustments
Biography
José Zubizarreta, PhD, is an associate professor in the Department of Health Care Policy at Harvard Medical School and in the Department Biostatistics at Harvard University. He is a Fellow of the American Statistical Association, and is a recipient of the Kenneth Rothman Award, the William Cochran Award, and the Tom Ten Have Memorial Award.
Elizabeth A. Stuart, Ph.D. is Bloomberg Professor of American Health in the Department of Mental Health, the Department of Biostatistics and the Department of Health Policy and Management at Johns Hopkins Bloomberg School of Public Health. She is a Fellow of the American Statistical Association, and she received the mid-career award from the Health Policy Statistics Section of the ASA, the Gertrude Cox Award for applied statistics, Harvard University’s Myrto Lefkopoulou Award for excellence in Biostatistics, and the Society for Epidemiologic Research Marshall Joffe Epidemiologic Methods award.
Dylan Small, PhD is the Universal Furniture Professor in the Department of Statistics and Data Science of the Wharton School of the University of Pennsylvania. He is a Fellow of the American Statistical Association and an Institute of Mathematical Statistics Medallion Lecturer.
Paul R. Rosenbaum is emeritus professor of Statistics and Data Science at the Wharton School of the University of Pennsylvania. From the Committee of Presidents of Statistical Societies, he received the R. A. Fisher Award and the George W. Snedecor Award. He is the author of several books, including Design of Observational Studies and Replication and Evidence Factors in Observational Studies.
"Edited and written by many prominent researchers in the field, the book covers both classical and modern topics. Each chapter is self-contained, making it a great reference book. The book is organized in a way that related topics are clustered together, enabling readers to easily navigate and read chapter by chapter. Overall, I enjoyed reading this book very much. [...] The book contains numerous real-data examples that aid readers in understanding the concepts and methods. Additionally, many chapters discuss the computational implementation of the corresponding methods. I am confident that researchers and practitioners will find this book to be an excellent resource for adjustment methods."
-Raymond K.W. Wong in Journal of the American Statistical Association, December 2023
