Comparar métodos

	Evaluación de políticas mediante emparejamiento por puntuación de propensión	Coarsened Exact Matching (CEM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	1983; policy evaluation adaptation 1997	2011-2012
Autor original	Rosenbaum & Rubin (1983); Heckman, Ichimura & Todd (1997) for program/policy evaluation application	Iacus, King, & Porro
Tipo	Quasi-experimental matching estimator	Matching / causal inference
Fuente seminal	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.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Alias	PSM policy evaluation, policy PSM, propensity matching for program evaluation, PSM treatment evaluation	CEM, coarsened matching, monotonic imbalance bounding matching
Relacionados	6	6
Resumen	Policy evaluation propensity score matching applies the propensity score framework — originally developed by Rosenbaum and Rubin (1983) and operationalized for program evaluation by Heckman et al. (1997) — to estimate the causal effect of a policy intervention. It constructs a credible comparison group from non-participants by matching them to participants on their estimated probability of receiving the treatment, enabling unbiased effect estimation without random assignment.	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.