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	Estymator dopasowania heterogenicznych efektów przyczynowych	Estymacja podwójnie odporna (AIPW)
Dziedzina	Wnioskowanie przyczynowe	Wnioskowanie przyczynowe
Rodzina	Regression model	Regression model
Rok powstania	1997-2006	2005
Twórca	Heckman, Ichimura & Todd; Abadie & Imbens	Robins & Rotnitzky; Bang & Robins
Typ	Causal inference / nonparametric matching	Semiparametric causal estimator
Źródło pierwotne	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.	Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.
Inne nazwy	HTE matching, subgroup matching estimator, conditional matching estimator, CATE matching	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Pokrewne	6	5
Podsumowanie	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.	Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.