Võrdle meetodeid

	Heterogeneous Treatment Effect Entropy Balancing	Entropy Balancing
Valdkond	Põhjuslik järeldamine	Põhjuslik järeldamine
Perekond	Regression model	Regression model
Tekkeaasta	2012-2016	2012
Looja	Hainmueller (2012) for entropy balancing; Athey & Imbens (2016) for heterogeneous effect estimation	Jens Hainmueller
Tüüp	Causal inference / heterogeneous effect estimation	Covariate-balancing reweighting
Algallikas	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.
Rööpnimetused	HTE entropy balancing, CATE with entropy balancing, heterogeneous effects EB, subgroup entropy balancing	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Seotud	5	6
Kokkuvõte	Heterogeneous Treatment Effect Entropy Balancing combines entropy balancing — a preprocessing step that reweights control units to match the treatment group on covariate moments — with methods that estimate how the treatment effect varies across subgroups or individuals. It produces covariate-balanced weights without parametric propensity models, then uses those weights to estimate conditional average treatment effects (CATEs) across moderating variables.	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.