Compara mètodes

	Coarsened Exact Matching per Efectes de Tractament Heterogenis	Equilibri d'Entropia
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2012-2013	2012
Autor original	Iacus, King & Porro (CEM foundation, 2012); subgroup HTE extensions by Imai & colleagues	Jens Hainmueller
Tipus	Matching-based causal inference with subgroup CATE estimation	Covariate-balancing reweighting
Font seminal	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	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.
Àlies	HTE-CEM, CEM with CATE estimation, subgroup CEM, coarsened exact matching with effect heterogeneity	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Relacionats	5	6
Resum	Heterogeneous treatment effect coarsened exact matching (HTE-CEM) extends the coarsened exact matching framework to estimate how treatment effects vary across subgroups or individual characteristics. After CEM creates balanced strata by coarsening continuous covariates into bins and exactly matching units within each bin, conditional average treatment effects (CATEs) are computed within or across these strata, revealing where treatment works, for whom, and by how much.	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.