Comparer des méthodes

	Pondération par l'inverse de la probabilité de traitement (IPW / IPTW)	Appariement par score de propension
Domaine	Inférence causale	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	2000	1983
Auteur d'origine	Robins, Hernán & Brumback	Paul Rosenbaum and Donald Rubin
Type	Causal inference weighting estimator	Method
Source fondatrice	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.	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	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting	PSM, propensity score weighting, covariate balance
Apparentées	5	3
Résumé	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.	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.