Comparar métodos

	Balanceamento por Entropia	Coarsened Exact Matching (CEM)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2012	2011-2012
Autor original	Jens Hainmueller	Iacus, King, & Porro
Tipo	Covariate-balancing reweighting	Matching / causal inference
Fonte 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.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Outros nomes	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing	CEM, coarsened matching, monotonic imbalance bounding matching
Relacionados	6	6
Resumo	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.	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.