Compară metode

	Echilibrarea prin entropie pentru evaluarea politicilor	Potrivirea scorului de propensitate
Domeniu	Inferență cauzală	Statistică pentru cercetare
Familie	Regression model	Process / pipeline
Anul apariției	2012	1983
Autorul original	Jens Hainmueller	Paul Rosenbaum and Donald Rubin
Tip	Preprocessing / reweighting estimator	Method
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.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Denumiri alternative	Entropy Balancing, EB Weighting, Maximum-Entropy Reweighting, Hainmueller Balancing	PSM, propensity score weighting, covariate balance
Înrudite	4	3
Rezumat	Entropy balancing is a maximum-entropy reweighting method that assigns weights to control-group units so that their weighted covariate moments exactly match those of the treated group. Introduced by Hainmueller (2012), it provides exact balance on specified moments without iterative propensity-score trimming, making it a powerful preprocessing tool for causal policy evaluation in observational studies.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.