Comparar métodos

	Equilibrio Entrópico para Efectos de Tratamiento Heterogéneos	Estimación Doblemente Robusta (AIPW)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2012-2016	2005
Autor original	Hainmueller (2012) for entropy balancing; Athey & Imbens (2016) for heterogeneous effect estimation	Robins & Rotnitzky; Bang & Robins
Tipo	Causal inference / heterogeneous effect estimation	Semiparametric causal estimator
Fuente seminal	Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.	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 entropy balancing, CATE with entropy balancing, heterogeneous effects EB, subgroup entropy balancing	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Relacionados	5	5
Resumen	Heterogeneous Treatment Effect Entropy Balancing combines entropy balancing — a preprocessing step that reweights control units to match the treatment group on covariate moments — with methods that estimate how the treatment effect varies across subgroups or individuals. It produces covariate-balanced weights without parametric propensity models, then uses those weights to estimate conditional average treatment effects (CATEs) across moderating variables.	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.