方法对比
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| 政策评估匹配估计量× | 粗化精确匹配 (CEM)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1998-2006 | 2011-2012 |
| 提出者≠ | Heckman, Ichimura & Todd; Abadie & Imbens | Iacus, King, & Porro |
| 类型≠ | Non-parametric causal estimator | Matching / causal inference |
| 开创性文献≠ | Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267. DOI ↗ | Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24. DOI ↗ |
| 别名≠ | matching estimator, program evaluation matching, treatment effect matching, Abadie-Imbens estimator | CEM, coarsened matching, monotonic imbalance bounding matching |
| 相关 | 6 | 6 |
| 摘要≠ | The policy evaluation matching estimator estimates the causal effect of a program or policy on treated units by pairing each participant with one or more non-participants who share similar pre-treatment characteristics. Developed rigorously by Heckman, Ichimura & Todd (1998) and Abadie & Imbens (2006), it avoids parametric outcome models and is the standard non-parametric tool for program and policy evaluation. | Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model. |
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