Comparar métodos

	Coarsened Exact Matching (CEM)	Ponderação por Escore de Propensão (PEP / IPW)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2011-2012	1983 (propensity score); 2003 (efficient IPW estimator)
Autor original	Iacus, King, & Porro	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Tipo	Matching / causal inference	Causal inference / reweighting
Fonte seminal	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
Outros nomes	CEM, coarsened matching, monotonic imbalance bounding matching	PSW, inverse probability weighting, IPW, propensity-based weighting
Relacionados	6	6
Resumo	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.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).