Comparar métodos

	Estimador de Emparejamiento Multiperiódico	Coarsened Exact Matching (CEM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2005	2011-2012
Autor original	Abadie (2005); Imbens & Wooldridge (2009)	Iacus, King, & Porro
Tipo	Quasi-experimental / causal inference	Matching / causal inference
Fuente seminal	Abadie, A. (2005). Semiparametric Difference-in-Differences Estimators. Review of Economic Studies, 72(1), 1-19.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Alias	panel matching estimator, longitudinal matching, multi-wave matching, repeated-cross-section matching	CEM, coarsened matching, monotonic imbalance bounding matching
Relacionados	6	6
Resumen	The multi-period matching estimator extends the standard matching framework to settings with multiple time periods, pairing each treated unit to similar untreated units based on pre-treatment covariates or propensity scores, then using within-pair before-after differences to estimate the average treatment effect on the treated (ATT). Leveraging repeated observations, it simultaneously controls for observed confounders and time-invariant unobserved heterogeneity.	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.