Comparer des méthodes

	Appariement par Score de Propension dans la Recherche en Éducation	Pondération par l'inverse de la probabilité de traitement (IPW / IPTW)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1983 (foundational); education adoption widespread from late 1990s	2000
Auteur d'origine	Rosenbaum & Rubin (1983); widely adopted in education research via Shadish, Cook & Campbell (2002)	Robins, Hernán & Brumback
Type	Quasi-experimental / matching-based causal inference	Causal inference weighting estimator
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.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Alias	PSM in education, educational PSM, PSM for program evaluation in schools, propensity matching education	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Apparentées	5	5
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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.