Causal Inference for Statistics, Social and Biomedical Sciences: An Introduction

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.