Sammenlign metoder

	Heterogen Behandlingseffekt Entropivægtning	Entropy Balancing
Fagområde	Kausal inferens	Kausal inferens
Familie	Regression model	Regression model
Oprindelsesår	2012-2016	2012
Ophavsperson	Hainmueller (2012) for entropy balancing; Athey & Imbens (2016) for heterogeneous effect estimation	Jens Hainmueller
Type	Causal inference / heterogeneous effect estimation	Covariate-balancing reweighting
Oprindelig kilde	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.
Aliasser	HTE entropy balancing, CATE with entropy balancing, heterogeneous effects EB, subgroup entropy balancing	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Relaterede	5	6
Resumé	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.