방법 비교

	공간 주변 구조 모형	공간 성향 점수 매칭
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2000s–2010s	2000s
창시자	Robins, Hernan & Brumback (MSM foundation, 2000); spatial extensions developed in spatial epidemiology literature	Extension of Rosenbaum & Rubin (1983) PSM to spatial settings; spatial adaptation developed in applied econometrics and epidemiology literature from the 2000s onward
유형	Causal inference / spatial weighting	Quasi-experimental matching estimator
원전	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
별칭	Spatial MSM, Geospatial MSM, Spatial IPW-MSM, Space-time marginal structural model	Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching
관련	6	6
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.	Spatial Propensity Score Matching (Spatial PSM) extends the classic propensity score matching framework to settings where units are embedded in geographic space and treatment assignment or outcomes may be spatially correlated. By incorporating spatial covariates and adjacency structure into the propensity model and matching procedure, it produces causal estimates that account for geographic confounding and spillover effects.