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| 异质性处理效应匹配估计器× | 熵平衡× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1997-2006 | 2012 |
| 提出者≠ | Heckman, Ichimura & Todd; Abadie & Imbens | Jens Hainmueller |
| 类型≠ | Causal inference / nonparametric matching | Covariate-balancing reweighting |
| 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 ↗ | 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 ↗ |
| 别名 | HTE matching, subgroup matching estimator, conditional matching estimator, CATE matching | EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing |
| 相关 | 6 | 6 |
| 摘要≠ | The Heterogeneous Treatment Effect (HTE) Matching Estimator extends standard matching to recover how treatment impacts differ across subgroups or covariate values. Rather than reporting a single average treatment effect, it pairs treated and control units on observed characteristics and then estimates the conditional average treatment effect (CATE) as a function of those characteristics — revealing who benefits most, least, or not at all. | Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step. |
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