Comparer des méthodes

	Pondération Inverse de Probabilité Robuste (PIP Robuste)	Appariement par score de propension
Domaine	Inférence causale	Statistiques de recherche
Famille	Regression model	Process / pipeline
Année d'origine	2000-2004	1983
Auteur d'origine	Lunceford & Davidian (2004); Robins, Hernán & Brumback (2000)	Paul Rosenbaum and Donald Rubin
Type	Causal weighting estimator	Method
Source fondatrice	Lunceford, J. K., & Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Statistics in Medicine, 23(19), 2937-2960.	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	Robust IPW, Stabilized IPW, Trimmed IPW, Variance-robust IPW	PSM, propensity score weighting, covariate balance
Apparentées	5	3
Résumé	Robust Inverse Probability Weighting is a causal inference estimator that reweights observed units by stabilized or trimmed propensity score weights, then applies sandwich or bootstrap variance estimation to guard against model misspecification, extreme weights, and inflated standard errors. It extends standard IPW to improve finite-sample performance and inferential reliability in observational studies.	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.