Võrdle meetodeid

	Ruumilised piiratud struktuurmudelid	Ruumi kaksikindlalt robustne estimatsioon
Valdkond	Põhjuslik järeldamine	Põhjuslik järeldamine
Perekond	Regression model	Regression model
Tekkeaasta	2000s–2010s	2010s–2020s
Looja	Robins, Hernan & Brumback (MSM foundation, 2000); spatial extensions developed in spatial epidemiology literature	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature
Tüüp	Causal inference / spatial weighting	Semiparametric causal estimator
Algallikas	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Papadogeorgou, G., Mealli, F., & Zigler, C. M. (2019). Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics, 75(3), 778-787.
Rööpnimetused	Spatial MSM, Geospatial MSM, Spatial IPW-MSM, Space-time marginal structural model	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation
Seotud	6	5
Kokkuvõte	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 doubly robust estimation is a semiparametric causal inference method that combines propensity score weighting with outcome regression modeling — providing protection against misspecification of either component — while explicitly accounting for spatial autocorrelation among units. It extends the classical augmented inverse probability weighting (AIPW) estimator to settings where treatment assignment and outcomes are geographically clustered or spatially dependent.