方法对比
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| 多期粗糙化精确匹配× | 匹配估计量× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2012–2021 | 1973 |
| 提出者≠ | Iacus, King & Porro (CEM, 2012); extended to multi-period panel settings | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| 类型≠ | Non-parametric matching / causal inference | Nonparametric matching / causal inference |
| 开创性文献≠ | Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24. DOI ↗ | Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267. DOI ↗ |
| 别名 | Multi-period CEM, Longitudinal CEM, Panel CEM, Multi-wave CEM | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| 相关 | 6 | 6 |
| 摘要≠ | Multi-period Coarsened Exact Matching (multi-period CEM) extends the CEM framework of Iacus, King, and Porro to longitudinal data with multiple pre- and post-treatment periods. It bins continuous covariates into coarsened categories, matches treated and control units that fall into the same cells across all relevant time periods, and then estimates a weighted average treatment effect that accounts for temporal structure. | The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome. |
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