Linganisha mbinu

	Modeli ya Miundo ya Kimarufuku ya Sera (MSM)	Mfumo wa Kielelezo wa Uhusiano (MSM)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2000	2000
Mwanzilishi	James M. Robins, Miguel A. Hernan, Babette Brumback	James M. Robins, Miguel A. Hernan, Babette Brumback
Aina	Causal inference / weighted regression	Causal model / semiparametric weighting
Chanzo asilia	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.
Majina mbadala	MSM for policy evaluation, policy MSM, causal MSM, structural policy weighting model	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Zinazohusiana	6	5
Muhtasari	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.