Compara mètodes

	Entropy Balancing	Mètode del Control Sintètic (SCM)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2012	2003–2010
Autor original	Jens Hainmueller	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tipus	Preprocessing / reweighting estimator	Quasi-experimental causal inference
Font seminal	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.	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
Àlies	Entropy Balancing, EB Weighting, Maximum-Entropy Reweighting, Hainmueller Balancing	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Relacionats	4	4
Resum	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.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.