Comparar métodos

	Coarsened Exact Matching (CEM)	Equilibrio por Entropía
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2011-2012	2012
Autor original	Iacus, King, & Porro	Jens Hainmueller
Tipo	Matching / causal inference	Covariate-balancing reweighting
Fuente 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.
Alias	CEM, coarsened matching, monotonic imbalance bounding matching	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Relacionados	6	6
Resumen	Coarsened Exact Matching is a preprocessing method that achieves covariate balance by temporarily coarsening continuous variables into bins, exactly matching treated and control units within those bins, and then discarding all unmatched units. Introduced by Iacus, King, and Porro (2011, 2012), it bounds imbalance on each covariate independently, yielding a matched sample on which any estimator can be applied without relying on a propensity score model.	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.