Comparer des méthodes

	Évaluation d'impact contrefactuelle en recherche sur l'éducation	Appariement par score de propension
Domaine	Inférence causale	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	2000s–2010s	1983
Auteur d'origine	Blundell & Costa Dias; formalized for EU education policy by the European Commission Joint Research Centre	Paul Rosenbaum and Donald Rubin
Type	Quasi-experimental causal inference framework	Method
Source fondatrice	Blundell, R., & Costa Dias, M. (2002). Alternative approaches to evaluation in empirical microeconomics. Portuguese Economic Journal, 1(2), 91-115.	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.
Alias	CIE in education, counterfactual program evaluation, causal impact evaluation, education policy impact evaluation	PSM, propensity score weighting, covariate balance
Apparentées	5	3
Résumé	Counterfactual impact evaluation (CIE) is the systematic application of causal inference designs — such as difference-in-differences, regression discontinuity, matching, and instrumental variables — to measure the genuine effect of education programs, policies, or interventions by constructing a credible counterfactual: what would have happened to participants had they not been treated.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.