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Causal inference for statistics, social, and biomedical sciences : an introduction / Guido W. Imbens, Donald B. Rubin.

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Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the...

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Bibliographic Details
Online Access: Full Text (via Cambridge)
Main Authors: Imbens, Guido (Author), Rubin, Donald B. (Author)
Format: Electronic eBook
Language:English
Published: New York, NY : Cambridge University Press, 2015.
Subjects:
Social sciences > Research.
Causation.
Inference.
Table of Contents:
  • Part I. Introduction: 1. The basic framework: potential outcomes, stability, and the assignment mechanism
  • 2. A brief history of the potential-outcome approach to causal inference
  • 3. A taxonomy of assignment mechanisms
  • Part II. Classical Randomized Experiments: 4. A taxonomy of classical randomized experiments
  • 5. Fisher's exact P-values for completely randomized experiments
  • 6. Neyman's repeated sampling approach to completely randomized experiments
  • 7. Regression methods for completely randomized experiments
  • 8. Model-based inference in completely randomized experiments
  • 9. Stratified randomized experiments
  • 10. Paired randomized experiments
  • 11. Case study: an experimental evaluation of a labor-market program
  • Part III. Regular Assignment Mechanisms: Design: 12. Unconfounded treatment assignment
  • 13. Estimating the propensity score
  • 14. Assessing overlap in covariate distributions
  • 15. Design in observational studies: matching to ensure balance in covariate distributions
  • 16. Design in observational studies: trimming to ensure balance in covariate distributions
  • Part IV. Regular Assignment Mechanisms: Analysis: 17. Subclassification on the propensity score
  • 18. Matching estimators (Card-Krueger data)
  • 19. Estimating the variance of estimators under unconfoundedness
  • 20. Alternative estimands
  • Part V. Regular Assignment Mechanisms: Supplementary Analyses: 21. Assessing the unconfoundedness assumption
  • 22. Sensitivity analysis and bounds
  • Part VI. Regular Assignment Mechanisms with Noncompliance: Analysis: 23. Instrumental-variables analysis of randomized experiments with one-sided noncompliance
  • 24. Instrumental-variables analysis of randomized experiments with two-sided noncompliance
  • 25. Model-based analyses with instrumental variables
  • Part VII. Conclusion: 26. Conclusions and extensions.

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