Comparar métodos

	Heterogeneous Treatment Effect Inverse Probability Weighting	Estimación Doblemente Robusta (AIPW)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2003–2015	2005
Autor original	Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli	Robins & Rotnitzky; Bang & Robins
Tipo	Causal inference / weighted regression	Semiparametric causal estimator
Fuente seminal	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189.	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.
Alias	HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Relacionados	5	5
Resumen	HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseudo-population in which treatment is independent of background characteristics, and then estimates conditional average treatment effects (CATEs) as a function of those characteristics.	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.