방법 비교

	공간 주변 구조 모형	Marginal Structural Model (MSM)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2000s–2010s	2000
창시자	Robins, Hernan & Brumback (MSM foundation, 2000); spatial extensions developed in spatial epidemiology literature	James M. Robins, Miguel A. Hernan, Babette Brumback
유형	Causal inference / spatial weighting	Causal model / semiparametric weighting
원전	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.
별칭	Spatial MSM, Geospatial MSM, Spatial IPW-MSM, Space-time marginal structural model	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
관련	6	5
요약	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.