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.
Author(s): José R. Zubizarreta, Elizabeth A. Stuart, Dylan S. Small, Paul R. Rosenbaum
Series: Chapman & Hall/CRC Handbooks of Modern Statistical Methods
Publisher: CRC Press/Chapman & Hall
Year: 2023
Language: English
Pages: 633
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Contributors
About the Editors
I. 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. Generalizability and Transportability
II. 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
III. 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
IV. Outcome Models, Machine Learning and Related Approaches
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
V. Beyond Adjustments
24. How to Be a Good Critic of an Observational Study
25. Sensitivity Analysis
26. Evidence Factors
Index
