Handbook of matching and weighting adjustments for causal inference
著者
書誌事項
Handbook of matching and weighting adjustments for causal inference
（Handbooks of modern statistical methods / Series editors, Garrett Fitzmaurice）
CRC Press, 2023
- : hbk
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注記
Other editors: Elizabeth A. Stuart, Dylan Small, Paul R. Rosenbaum
Includes bibliographical references and index
内容説明・目次
内容説明
Introduce the steps from association to causation that follow after adjustments are complete.
Gives good analysis of weighting and matching in model-based adjustments.
Useful for thos who examine evidence of the effects on human beings of treatments, policies or exposures.
目次
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
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