Comparer des méthodes

	Évaluation de politiques par devis-régression (Regression Discontinuity Design)	Appariement par score de propension
Domaine	Inférence causale	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	1960; policy evaluation applications widespread from 2000s	1983
Auteur d'origine	Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010)	Paul Rosenbaum and Donald Rubin
Type	Quasi-experimental causal design	Method
Source fondatrice	Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.	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	Policy RDD, RD design in policy evaluation, regression discontinuity policy analysis, RDD policy impact	PSM, propensity score weighting, covariate balance
Apparentées	5	3
Résumé	Policy Evaluation Regression Discontinuity Design (Policy RDD) exploits a known eligibility threshold in a policy rule to estimate the causal effect of that policy on outcomes. Units just below the cutoff serve as a credible comparison group for units just above it, making RDD one of the most transparent quasi-experimental strategies for assessing what a policy actually achieves.	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.