Comparer des méthodes

	Pondération par score de propension pour l'évaluation des politiques	Estimation doublement robuste (AIPW)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1983/2003	2005
Auteur d'origine	Rosenbaum & Rubin (1983); extended to policy evaluation by Hirano, Imbens & Ridder (2003)	Robins & Rotnitzky; Bang & Robins
Type	Quasi-experimental causal inference	Semiparametric causal estimator
Source fondatrice	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	PSW policy evaluation, inverse probability weighting for policy, IPW policy evaluation, policy PSW	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Apparentées	6	5
Résumé	Policy evaluation propensity score weighting applies inverse-probability weighting to observational data to estimate the causal effect of a policy program. By reweighting participants and non-participants so they resemble a target population, it removes selection bias from voluntary or administratively allocated program assignment without requiring randomization.	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.