Confronta i metodi

	Entropy Balancing per la Valutazione delle Politiche	Abbinamento del punteggio di propensione
Campo	Inferenza causale	Statistica per la ricerca
Famiglia	Regression model	Process / pipeline
Anno di origine	2012	1983
Ideatore	Jens Hainmueller	Paul Rosenbaum and Donald Rubin
Tipo	Preprocessing / reweighting estimator	Method
Fonte seminale	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.
Alias	Entropy Balancing, EB Weighting, Maximum-Entropy Reweighting, Hainmueller Balancing	PSM, propensity score weighting, covariate balance
Correlati	4	3
Sintesi	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.