Comparer des méthodes

	Modèle Structurel Marginal pour l'Évaluation des Politiques	Modèle structurel marginal (MSM)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2000	2000
Auteur d'origine	James M. Robins, Miguel A. Hernan, Babette Brumback	James M. Robins, Miguel A. Hernan, Babette Brumback
Type	Causal inference / weighted regression	Causal model / semiparametric weighting
Source fondatrice	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550–560.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Alias	MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Apparentées	6	5
Résumé	A Policy Evaluation Marginal Structural Model (MSM) is a causal inference framework that estimates the population-average effect of a policy by using inverse probability weighting to create a pseudo-population in which treatment assignment is independent of measured confounders, enabling unbiased comparison of potential outcomes under different policy scenarios from observational data.	A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.