Comparar métodos

	Propensity Score Matching em Pesquisa Educacional	Coarsened Exact Matching (CEM)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	1983 (foundational); education adoption widespread from late 1990s	2011-2012
Autor original	Rosenbaum & Rubin (1983); widely adopted in education research via Shadish, Cook & Campbell (2002)	Iacus, King, & Porro
Tipo	Quasi-experimental / matching-based causal inference	Matching / causal inference
Fonte 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.
Outros nomes	PSM in education, educational PSM, PSM for program evaluation in schools, propensity matching education	CEM, coarsened matching, monotonic imbalance bounding matching
Relacionados	5	6
Resumo	Propensity Score Matching (PSM) in education research is a quasi-experimental technique that creates comparable treatment and control groups from observational student, teacher, or school data. By balancing groups on observed background characteristics, it enables credible causal estimates of educational interventions — such as tutoring programs, school choice policies, or teacher professional development — when random assignment is infeasible.	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.