Compară metode

	Potrivire Exactă Coarsened Bayesiană	Ponderarea prin entropie
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	2011-2012	2012
Autorul original	Iacus, King & Porro (CEM framework, 2012); Bayesian extensions by Hill and subsequent authors	Jens Hainmueller
Tip	Quasi-experimental matching with Bayesian inference	Covariate-balancing reweighting
Sursa seminală	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	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	Bayesian CEM, BCEM, Bayesian monotonic imbalance bounding matching	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Înrudite	6	6
Rezumat	Bayesian Coarsened Exact Matching (Bayesian CEM) combines the coarsening-and-exact-matching framework of Iacus, King, and Porro with Bayesian posterior inference. Covariates are discretised into coarser bins so that treated and control units can be matched exactly within those bins, and Bayesian priors are then placed on the treatment-effect parameters to produce full posterior distributions over the causal estimand rather than a single point estimate.	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.