Compară metode

	Echilibrarea Entropică Bayesiană	Ponderarea prin entropie
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	2012-2020s	2012
Autorul original	Hainmueller (2012, entropy balancing foundation); Bayesian extension developed in subsequent causal inference literature	Jens Hainmueller
Tip	Weighting-based causal estimator with Bayesian uncertainty quantification	Covariate-balancing reweighting
Sursa seminală	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.	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.
Denumiri alternative	BEB, Bayesian EB, Bayesian covariate balancing, entropy balancing with Bayesian inference	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Înrudite	6	6
Rezumat	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.	Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.