Comparer des méthodes

	Estimation robuste double multi-périodes	Estimation doublement robuste (AIPW)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1994-2021	2005
Auteur d'origine	Robins, Rotnitzky, and Zhao; extended by Bang & Robins (2005) and Callaway & Sant'Anna (2021)	Robins & Rotnitzky; Bang & Robins
Type	Semiparametric causal estimator	Semiparametric causal estimator
Source fondatrice	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	longitudinal DR estimation, multi-period DR, multi-wave doubly robust, sequential doubly robust estimation	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Apparentées	6	5
Résumé	Multi-period doubly robust (DR) estimation extends the classic doubly robust approach to longitudinal settings with multiple treatment periods and time points. It combines an outcome regression model and a propensity score model for each period, retaining consistency of the causal effect estimate as long as at least one of the two models is correctly specified at every time point.	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.