Methoden vergleichen

	Bayesian Entropy Balancing	Doubly Robust Estimation (AIPW)
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	2012-2020s	2005
Urheber	Hainmueller (2012, entropy balancing foundation); Bayesian extension developed in subsequent causal inference literature	Robins & Rotnitzky; Bang & Robins
Typ	Weighting-based causal estimator with Bayesian uncertainty quantification	Semiparametric causal estimator
Wegweisende Quelle	Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.	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.
Aliasnamen	BEB, Bayesian EB, Bayesian covariate balancing, entropy balancing with Bayesian inference	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Verwandt	6	5
Zusammenfassung	Bayesian Entropy Balancing extends the classical entropy balancing approach — which reweights control units so that their covariate moments match the treated group exactly — by embedding this reweighting within a Bayesian framework. This allows researchers to incorporate prior beliefs about treatment propensities, propagate parameter uncertainty into the final causal estimate, and obtain credible intervals rather than only classical confidence intervals.	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.