Compara mètodes

	Model Estructural Marginal per a l'Avaluació de Polítiques	Model Estructural Marginal (MSM)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2000	2000
Autor original	James M. Robins, Miguel A. Hernan, Babette Brumback	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipus	Causal inference / weighted regression	Causal model / semiparametric weighting
Font seminal	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.
Àlies	MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relacionats	6	5
Resum	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.