Handbook of Matching and Weighting Adjustments for Causal Inference

Part I: Conceptual issues

1. 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
2. Propensity score
   Paul R. Rosenbaum
3. Generalization and Transportability
   Elizabeth Tipton and Erin Hartman

Part II: Matching

4. Optimization techniques in multivariate matching
   Paul R. Rosenbaum and José R. Zubizarreta
5. Optimal Full matching
   Mark M. Fredrickson and Ben Hansen
6. Fine balance and its variations in modern optimal matching
   Samuel D. Pimentel
7. Matching with instrumental variables
   Mike Baiocchi and Hyunseung Kang
8. Covariate Adjustment in Regression Discontinuity Designs
   Matias D. Cattaneo, Luke Keele, Rocío Titiunik
9. Risk Set Matching
   Bo Lu and Robert A. Greevy, Jr.
10. Matching with Multilevel Data
    Samuel D. Pimentel and Luke Keele
11. Effect Modification in Observational Studies
    Kwonsang Lee and Jesse Y. Hsu
12. Optimal Nonbipartite Matching
    Robert A. Greevy, Jr. and Bo Lu
13. Matching Methods for Large Observational Studies
    Ruoqi Yu

Part III: Weighting

14. Overlap Weighting
    Fan Li
15. Covariate Balancing Propensity Score
    Kosuke Imai and Yang Ning
16. Balancing Weights for Causal Inference
    Eric R. Cohn, Eli Ben-Michael, Avi Feller, and José R. Zubizarreta
17. Assessing Principal Causal Effects Using Principal Score Methods
    Alessandra Mattei, Laura Forastiere, Fabrizia Mealli
18. Incremental Causal Effects: An Introduction and Review
    Matteo Bonvini, Alec McClean, Zach Branson and Edward H. Kennedy
19. Weighting Estimators for Causal Mediation
    Donna L. Coffman, Megan S. Schuler, Trang Q. Nguyen, and Daniel F. McCaffrey

Part IV: Machine Learning Adjustments

20. Machine Learning for Causal Inference
    Jennifer Hill, George Perrett and Vincent Dorie
21. Treatment Heterogeneity with Survival Outcomes
    Yizhe Xu, Nikolaos Ignatiadis, Erik Sverdrup, Scott Fleming, Stefan Wager, Nigam Shah
22. Why Machine Learning Cannot Ignore Maximum Likelihood Estimation
    Mark J. van der Laan and Sherri Rose
23. Bayesian Propensity Score methods and Related Approaches for Confounding Adjustment

Joseph Antonelli

Part V: Beyond Adjustments

24. How to Be a Good Critic of an Observational Study
    Dylan S. Small
25. Sensitivity Analysis
    C.B. Fogarty
26. Evidence Factors
    Bikram Karmakar

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

"The book benefits from a comprehensive collection of recent causal inference methods, offering a wide range of perspectives on weighting and matching techniques. While all the methods share the common goal of unbiased causal effect estimation in observational studies, each chapter clearly demonstrates its focus (eg, balancing covariates or using survival outcomes). In particular, each chapter includes data application examples at the end or incorporates application studies throughout. [...] I am grateful that this book contributes to expanding the accessibility of modern causal inference tools, bringing them together in a cohesive manner for researchers and educators who wish to learn, teach, and apply these methods to obtain unbiased causal evidence from —potentially messy and unkind—observational studies."
-Youjin Lee in Biometrics, September 2024