Comparar métodos

	Coarsened Exact Matching en Investigación Educativa	Coarsened Exact Matching (CEM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2012	2011-2012
Autor original	Iacus, King, & Porro	Iacus, King, & Porro
Tipo	Matching / quasi-experimental	Matching / causal inference
Fuente seminal	Iacus, S. M., King, G., & Porro, G. (2012). Causal inference without balance checking: Coarsened exact matching. Political Analysis, 20(1), 1-24.	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.
Alias	CEM in education, CEM for educational studies, exact matching education, coarsened matching educational data	CEM, coarsened matching, monotonic imbalance bounding matching
Relacionados	4	6
Resumen	Coarsened Exact Matching (CEM) is a pre-processing matching strategy that reduces imbalance between treated and comparison groups before outcome analysis. In education research it is used to create balanced comparison groups from administrative records, survey data, or quasi-experimental study designs — for example comparing students who received an intervention against comparable students who did not, without relying on randomisation.	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.