Comparer des méthodes

	Pondération Inverse de Probabilité Robuste (PIP Robuste)	Estimation doublement robuste (AIPW)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2000-2004	2005
Auteur d'origine	Lunceford & Davidian (2004); Robins, Hernán & Brumback (2000)	Robins & Rotnitzky; Bang & Robins
Type	Causal weighting estimator	Semiparametric causal estimator
Source fondatrice	Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine, 23(19), 2937-2960.	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	Robust IPW, Stabilized IPW, Trimmed IPW, Variance-robust IPW	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Apparentées	5	5
Résumé	Robust Inverse Probability Weighting is a causal inference estimator that reweights observed units by stabilized or trimmed propensity score weights, then applies sandwich or bootstrap variance estimation to guard against model misspecification, extreme weights, and inflated standard errors. It extends standard IPW to improve finite-sample performance and inferential reliability in observational studies.	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.