Linganisha mbinu

	Mfumo wa Kimahesabu wa Kimahesabu wa Kimahesabu	Mfumo wa Kielelezo wa Uhusiano (MSM)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2000s–2010s	2000
Mwanzilishi	Robins, Hernan & Brumback (MSM foundation, 2000); spatial extensions developed in spatial epidemiology literature	James M. Robins, Miguel A. Hernan, Babette Brumback
Aina	Causal inference / spatial weighting	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	Spatial MSM, Geospatial MSM, Spatial IPW-MSM, Space-time marginal structural model	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Zinazohusiana	6	5
Muhtasari	The Spatial Marginal Structural Model (Spatial MSM) extends the classical marginal structural model to settings where units are geographically distributed and spatial dependencies — such as neighborhood spillovers, clustering, and spatial confounding — may bias causal estimates. It estimates causal effects of spatially varying exposures by constructing inverse probability weights that account for both individual covariates and spatial location, then fitting a weighted outcome model in the resulting pseudo-population.	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.