Comparer des méthodes

	Modèle structurel marginal dans la recherche en éducation	Pondération par l'inverse de la probabilité de traitement (IPW / IPTW)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2000 (method); 2006 (canonical education application)	2000
Auteur d'origine	James M. Robins, Miguel A. Hernán, Babette Brumback (epidemiology); Guanglei Hong & Stephen Raudenbush (education application)	Robins, Hernán & Brumback
Type	Causal inference / weighted regression model	Causal inference weighting estimator
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., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Alias	MSM, marginal structural model, MSM with inverse probability weighting, IPW-MSM	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Apparentées	5	5
Résumé	A marginal structural model (MSM) is a causal inference technique that uses inverse probability weighting to estimate the effect of a treatment or educational intervention that changes over time. Introduced by Robins, Hernán and Brumback (2000) in epidemiology and brought into education by Hong and Raudenbush (2006), MSMs handle time-varying confounding — a challenge that conventional regression cannot resolve.	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.