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	Ponderazione Bayesiana dell'Inverso della Probabilità	Stima a Doppia Robustezza (AIPW)
Campo	Inferenza causale	Inferenza causale
Famiglia	Regression model	Regression model
Anno di origine	2015	2005
Ideatore	Saarela, Stephens, Moodie & Klein (2015); Liao & Zigler (2020)	Robins & Rotnitzky; Bang & Robins
Tipo	Bayesian causal weighting estimator	Semiparametric causal estimator
Fonte seminale	Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On risk prediction and characterisation of treatment effects in a Bayesian framework using the propensity score. Statistics in Medicine, 34(14), 2170-2185.	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	Bayesian IPW, BIPW, Bayesian propensity-weighted estimation, Bayesian marginal structural weighting	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Correlati	6	5
Sintesi	Bayesian Inverse Probability Weighting (Bayesian IPW) extends the classical IPW estimator by placing prior distributions over the propensity-score model parameters and propagating that uncertainty into the causal-effect estimate. The result is a posterior distribution for the average treatment effect that fully accounts for both propensity-score estimation uncertainty and outcome-model uncertainty, enabling credible-interval inference rather than relying on asymptotic approximations.	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.