방법 비교

	공간적 조밀화된 정확 일치법 (Spatial CEM)	공간 이중 강건 추정
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2012 (CEM foundation); spatial extension in applied literature 2015-present	2010s–2020s
창시자	Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature
유형	Quasi-experimental matching estimator with spatial covariates	Semiparametric causal estimator
원전	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	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 CEM, Geographic CEM, Spatial exact matching, CEM with spatial covariates	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation
관련	6	5
요약	Spatial Coarsened Exact Matching applies the Coarsened Exact Matching framework to study designs involving geographic units — neighbourhoods, census tracts, municipalities, or grid cells. Covariates are coarsened into discrete bins and units are matched exactly on those bins, with spatial attributes (location, adjacency, geographic characteristics) incorporated as matching dimensions to control for spatial confounding.	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.