| 내용 |
Part I, Causality and Empirical Research in the Social Sciences. - 1, Introduction. - Part II, Counterfactuals, Potential Outcomes, and Causal Graphs. - 2, Counterfactuals and the potential-outcome model. - 3, Causal graphs. - Part 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. - Part 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. - Part V, Estimation When Causal Effects Are Not Point Identified by Observables. - 12, Distributional assumptions, set identification, and sensitivity analysis. - Part VI. Conclusions. - 13, Counterfactuals and the future of empirical research in observational social science.
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