Comparer des méthodes

	Appariement par Score de Propension dans la Recherche en Éducation	Estimateur par appariement
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1983 (foundational); education adoption widespread from late 1990s	1973
Auteur d'origine	Rosenbaum & Rubin (1983); widely adopted in education research via Shadish, Cook & Campbell (2002)	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Type	Quasi-experimental / matching-based causal inference	Nonparametric matching / causal inference
Source fondatrice	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.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Alias	PSM in education, educational PSM, PSM for program evaluation in schools, propensity matching education	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Apparentées	5	6
Résumé	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.	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.