Methoden vergelijken

	Marginaal Structureel Model voor Beleidsevaluatie	Marginaal Structureel Model (MSM)
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	2000	2000
Grondlegger	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
Oorspronkelijke bron	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.
Aliassen	MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Verwant	6	5
Samenvatting	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.