Salīdzināt metodes

	Beijesa entropijas balansēšana	Entropijas balansēšana
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2012-2020s	2012
Autors	Hainmueller (2012, entropy balancing foundation); Bayesian extension developed in subsequent causal inference literature	Jens Hainmueller
Tips	Weighting-based causal estimator with Bayesian uncertainty quantification	Covariate-balancing reweighting
Pirmavots	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.
Citi nosaukumi	BEB, Bayesian EB, Bayesian covariate balancing, entropy balancing with Bayesian inference	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Saistītās	6	6
Kopsavilkums	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.