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	Bayesowskie ważenie z wykorzystaniem wyniku skłonności	Estymacja podwójnie odporna (AIPW)
Dziedzina	Wnioskowanie przyczynowe	Wnioskowanie przyczynowe
Rodzina	Regression model	Regression model
Rok powstania	2009	2005
Twórca	McCandless, Gustafson & Austin	Robins & Rotnitzky; Bang & Robins
Typ	Bayesian causal weighting estimator	Semiparametric causal estimator
Źródło pierwotne	McCandless, L. C., Gustafson, P., & Austin, P. C. (2009). Bayesian propensity score analysis for observational data. Statistics in Medicine, 28(1), 94–112.	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.
Inne nazwy	Bayesian PSW, Bayesian IPW, Bayesian inverse probability weighting, Bayesian propensity weighting	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Pokrewne	6	5
Podsumowanie	Bayesian Propensity Score Weighting estimates causal treatment effects in observational data by combining a Bayesian model for the propensity score with inverse probability weighting. By placing a prior over propensity-score parameters and propagating posterior uncertainty through the weighting step, this approach yields fully probabilistic uncertainty intervals for the average treatment effect, accounting for the uncertainty in both the score model and the outcome.	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.