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| Heterogener Treatment-Effekt-Matching-Schätzer× | Matching-Schätzer× | |
|---|---|---|
| Fachgebiet | Kausale Inferenz | Kausale Inferenz |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1997-2006 | 1973 |
| Urheber≠ | Heckman, Ichimura & Todd; Abadie & Imbens | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| Typ≠ | Causal inference / nonparametric matching | Nonparametric matching / causal inference |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen | HTE matching, subgroup matching estimator, conditional matching estimator, CATE matching | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | 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. | 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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