Comparar métodos

	Evaluación de Políticas Estimación Doblemente Robusta	Estimación Doblemente Robusta (AIPW)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	1994-2005	2005
Autor original	Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)	Robins & Rotnitzky; Bang & Robins
Tipo	Semiparametric causal estimator	Semiparametric causal estimator
Fuente seminal	Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.	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	DR estimation for policy, augmented IPW for policy evaluation, AIPW policy evaluation, doubly robust policy analysis	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Relacionados	5	5
Resumen	Policy Evaluation Doubly Robust Estimation applies the doubly robust (DR) estimator to assess the causal effect of a public policy or programme. It combines a model of treatment assignment (propensity score) with a model of the outcome, and requires only one of the two models to be correctly specified to produce a consistent estimate of the average treatment effect, making it a resilient tool for programme evaluation.	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.