Comparer des méthodes

	Pondération par score de propension multi-périodes	Estimation doublement robuste (AIPW)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2000	2005
Auteur d'origine	Robins, Hernán, and Brumback (building on Robins' g-computation framework)	Robins & Rotnitzky; Bang & Robins
Type	Quasi-experimental causal inference	Semiparametric causal estimator
Source fondatrice	Hernán, M. A., & Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.	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 propensity score weighting, multi-wave PSW, time-varying propensity score weighting, sequential propensity score weighting	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Apparentées	5	5
Résumé	Multi-period propensity score weighting extends the standard propensity score weighting framework to settings with repeated measurements and time-varying treatments. It constructs stabilised inverse probability weights (IPW) at each time point so that the weighted sample resembles a sequence of randomised experiments, allowing unbiased estimation of causal effects under longitudinal confounding.	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.