Compara mètodes

	Heterogeneous Treatment Effect Inverse Probability Weighting	Estimació Doblement Robusta (AIPW)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2003–2015	2005
Autor original	Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli	Robins & Rotnitzky; Bang & Robins
Tipus	Causal inference / weighted regression	Semiparametric causal estimator
Font 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.
Àlies	HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Relacionats	5	5
Resum	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.