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| 面板数据熵平衡× | 面板数据倾向得分匹配× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2012 (cross-section); panel adaptation mid-2010s onward | 1997-1998 |
| 提出者≠ | Hainmueller (2012); extended to panel settings by subsequent applied econometric work | Heckman, Ichimura & Todd |
| 类型≠ | Covariate balancing / reweighting estimator | Matching / causal inference |
| 开创性文献≠ | Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46. DOI ↗ | Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an Econometric Evaluation Estimator. Review of Economic Studies, 65(2), 261-294. DOI ↗ |
| 别名 | EB-panel, panel entropy balancing, entropy reweighting in panel data, panel-EB | PSM with panel data, longitudinal PSM, panel PSM, difference-in-differences PSM |
| 相关≠ | 5 | 6 |
| 摘要≠ | Panel data entropy balancing extends Hainmueller's (2012) entropy balancing method to longitudinal settings. It computes unit-level weights for control observations so that their covariate moments exactly match those of the treatment group across panel periods, then plugs these weights into a weighted panel regression to estimate causal treatment effects without requiring a correctly specified propensity score model. | Panel data propensity score matching combines the bias-reduction of PSM with the longitudinal structure of panel data, enabling causal estimation of treatment effects by matching treated and control units on observable pre-treatment characteristics and then differencing within matched pairs over time. Developed in the framework of Heckman, Ichimura, and Todd (1998), it is especially valuable when randomisation is infeasible and both selection on observables and time-varying confounding must be addressed simultaneously. |
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