| 내용 |
1, Introduction. - 2, Counterfactual framework and assumptions. - 3, Conventional methods for data balancing. - 4, Sample selection and related models. - 5, Propensity score matching and related models. - 6, Propensity score subclassification. - 7, Propensity score weighting. - 8, Matching estimators. - 9, Propensity score analysis with nonparametric regression. - 10, Propensity score analysis of categorical or continuous treatments. - 11, Selection bias and sensitivity analysis. - 12, Concluding remarks.
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