方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 面板数据匹配估计量× | 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≠ | 1997-2021 | 1973 |
| 提出者≠ | Heckman, Ichimura & Todd (1997); Imai, Kim & Wang (2021) for panel extension | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| 类型≠ | Quasi-experimental causal estimator | Nonparametric matching / causal inference |
| 开创性文献≠ | Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies, 64(4), 605-654. 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 | panel matching, matching-on-panel-data, longitudinal matching estimator, PDME | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| 相关 | 6 | 6 |
| 摘要≠ | The panel data matching estimator identifies causal treatment effects by pairing each treated unit with one or more control units that share similar covariate histories in the pre-treatment periods. By exploiting the longitudinal structure of panel data, it controls for both observed time-varying confounders and stable unit characteristics, estimating the average treatment effect on the treated (ATT) without requiring a parallel-trends assumption. | 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. |
| ScholarGate数据集 ↗ |
|
|