Comparar métodos

	Emparejamiento Bayesiano por Puntuación de Propensión	Coarsened Exact Matching (CEM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2012	2011-2012
Autor original	Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983)	Iacus, King, & Porro
Tipo	Bayesian causal inference / matching	Matching / causal inference
Fuente seminal	Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Alias	Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting	CEM, coarsened matching, monotonic imbalance bounding matching
Relacionados	6	6
Resumen	Bayesian Propensity Score Matching (Bayesian PSM) extends classical propensity score matching by placing a prior distribution over the propensity model parameters and propagating posterior uncertainty through the matching and outcome stages. Introduced formally by Kaplan and Chen (2012), it offers a principled account of estimation uncertainty that frequentist matching commonly ignores, and allows incorporation of substantive prior knowledge about treatment selection.	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.