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Handbook of Matching and Weighting Adjustments for Causal Inference



  • Available for pre-order on March 14, 2023. Item will ship after April 4, 2023
ISBN 9780367609528
April 4, 2023 Forthcoming by Chapman & Hall
720 Pages 42 Color & 32 B/W Illustrations

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Book Description

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.

Table of Contents

Part 1: Conceptual issues  1. Overview of methods for adjustment and applications in the social and behavioral sciences: The role of study design  2. Propensity score  3. Generalization and Transportability  Part 2: Matching  4. Optimization techniques in multivariate matching  5. Optimal Full matching  6. Fine balance and its variations in modern optimal matching  7. Overview  8. Covariate Adjustment in Regression Discontinuity Designs  9. Risk Set Matching  10. Matching with Multilevel Data  11. Effect Modification in Observational Studies  12. Optimal Nonbipartite Matching  13. Matching Methods for Large Observational Studies  Part 3: Weighting  14. Overlap Weighting  15. Covariate Balancing Propensity Score  16. Balancing Weights for Causal Inference  17. Assessing Principal Causal Effects Using Principal Score Methods  18. Incremental Causal Effects: An Introduction and Review  19. Weighting Estimators for Causal Mediation  Part 4: Machine Learning Adjustments  20. Machine Learning for Causal Inference  21. Treatment Heterogeneity with Survival Outcomes  22. Why Machine Learning Cannot Ignore Maximum Likelihood Estimation  23. Bayesian Propensity Score methods and Related Approaches for Confounding Adjustment  Part 5: Beyond Adjustments  24. How to Be a Good Critic of an Observational Study  25. Sensitivity Analysis  26. Evidence Factors

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Editor(s)

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.