Comparer des méthodes

	Appariement par score de propension	Analyse de régression multiple
Domaine	Statistiques de recherche	Statistiques de recherche
Famille	Process / pipeline	Process / pipeline
Année d'origine	1983	1801
Auteur d'origine	Paul Rosenbaum and Donald Rubin	Carl Friedrich Gauss
Type	Method	Method
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.	Draper, N. R., & Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons.
Alias	PSM, propensity score weighting, covariate balance	MLR, multivariate regression, linear regression
Apparentées	3	4
Résumé	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.	Multiple regression analysis is a statistical method for modeling the relationship between a continuous dependent variable and two or more independent variables (predictors). Originating from Gauss's early 19th-century work and formalized by Draper and Smith (1966), it estimates linear equations predicting outcomes from multiple predictors while accounting for confounding relationships, making it indispensable in epidemiology, economics, psychology, and clinical research.