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I. Causality and Empirical Research in the Social Sciences
1. Introduction
II. Counterfactuals, Potential Outcomes, and Causal Graphs
2. Counterfactuals and the potential-outcome model
3. Causal graphs
III. Estimating Causal Effects by Conditioning on Observed Variables to Block Backdoor Paths
4. Models of causal exposure and identification criteria for conditioning estimators \
5. Matching estimators of causal effects
6. Regression estimators of causal effects
7. Weighted regression estimators of causal effects
IV. Estimating Causal Effects When Backdoor Conditioning Is Ineffective
8. Self-selection, heterogeneity, and causal graphs
9. Instrumental-variable estimators of causal effects
10. Mechanisms and causal explanation
11. Repeated observations and the estimation of causal effects
V. Estimation When Causal Effects Are Not Point Identified by Observables
12. Distributional assumptions, set identification, and sensitivity analysis
VI. Conclusions
13. Counterfactuals and the future of empirical research in observational social science.
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