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| Estymator dopasowania odpornego (dopasowanie skorygowane o błąd)× | Estymator dopasowujący× | |
|---|---|---|
| Dziedzina | Wnioskowanie przyczynowe | Wnioskowanie przyczynowe |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2006/2011 | 1973 |
| Twórca≠ | Abadie & Imbens | Rubin (1973); large-sample theory by Abadie & Imbens (2006) |
| Typ≠ | Causal inference / matching | Nonparametric matching / causal inference |
| Źródło pierwotne≠ | Abadie, A., & Imbens, G. W. (2011). Bias-Corrected Matching Estimators for Average Treatment Effects. Journal of Business & Economic Statistics, 29(1), 1-11. DOI ↗ | Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267. DOI ↗ |
| Inne nazwy | bias-corrected matching, Abadie-Imbens matching, AI matching estimator, robust nearest-neighbor matching | nearest-neighbor matching, NNM, matching on covariates, covariate matching |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | The robust matching estimator, developed by Abadie and Imbens (2006, 2011), extends nearest-neighbor matching by adding a regression-based bias correction that removes the finite-sample bias arising when matched units are not perfectly alike. It yields consistent, asymptotically normal estimates of average treatment effects with a heteroskedasticity-robust variance formula that is valid regardless of the number of continuous covariates. | 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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