Comparer des méthodes

	Estimateur par appariement robuste (appariement corrigé du biais)	Estimateur par appariement
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2006/2011	1973
Auteur d'origine	Abadie & Imbens	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Type	Causal inference / matching	Nonparametric matching / causal inference
Source fondatrice	Abadie, A., & Imbens, G. W. (2011). Bias-Corrected Matching Estimators for Average Treatment Effects. Journal of Business & Economic Statistics, 29(1), 1-11.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Alias	bias-corrected matching, Abadie-Imbens matching, AI matching estimator, robust nearest-neighbor matching	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Apparentées	6	6
Résumé	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.