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Handbook of Matching and Weighting Adjustments for Causal Inference de José R. Zubizarreta

Handbook of Matching and Weighting Adjustments for Causal Inference: Chapman & Hall/CRC Handbooks of Modern Statistical Methods

Editat de José R. Zubizarreta, Elizabeth A. Stuart, Dylan S. Small, Paul R. Rosenbaum
en Limba Engleză Hardback – 11 apr 2023
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.
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Specificații

ISBN-13: 9780367609528
ISBN-10: 0367609525
Pagini: 634
Ilustrații: 63 Tables, black and white; 41 Line drawings, color; 32 Line drawings, black and white; 1 Halftones, color; 42 Illustrations, color; 32 Illustrations, black and white
Dimensiuni: 178 x 254 x 37 mm
Greutate: 1.16 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Handbooks of Modern Statistical Methods

Public țintă

Postgraduate and Professional

Cuprins

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. Matching with instrumental variables  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

Notă biografică

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.

Recenzii

"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

Descriere

Multivariate matching and weighting are two modern forms of adjustment. 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.