방법 비교

	공간 성향 점수 매칭	공간 이중 강건 추정
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2000s	2010s–2020s
창시자	Extension of Rosenbaum & Rubin (1983) PSM to spatial settings; spatial adaptation developed in applied econometrics and epidemiology literature from the 2000s onward	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature
유형	Quasi-experimental matching estimator	Semiparametric causal estimator
원전	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.	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.
별칭	Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation
관련	6	5
요약	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.	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.