1st Edition

Handbook of Matching and Weighting Adjustments for Causal Inference

    634 Pages 42 Color & 32 B/W Illustrations
    by Chapman & Hall

    634 Pages 42 Color & 32 B/W Illustrations
    by Chapman & Hall

    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

    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
    4. Part II: Matching

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

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

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

      Part V: Beyond Adjustments

    28. How to Be a Good Critic of an Observational Study
      Dylan S. Small
    29. Sensitivity Analysis
      C.B. Fogarty
    30. Evidence Factors
      Bikram Karmakar


    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