方法对比

	空间粗粒化精确匹配 (Spatial CEM)	空间倾向得分匹配
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2012 (CEM foundation); spatial extension in applied literature 2015-present	2000s
提出者	Iacus, King & Porro (CEM foundation, 2012); extended to spatial contexts by applied spatial econometricians	Extension of Rosenbaum & Rubin (1983) PSM to spatial settings; spatial adaptation developed in applied econometrics and epidemiology literature from the 2000s onward
类型	Quasi-experimental matching estimator with spatial covariates	Quasi-experimental matching estimator
开创性文献	Iacus, S. M., King, G., & Porro, G. (2012). Causal Inference without Balance Checking: Coarsened Exact Matching. Political Analysis, 20(1), 1-24.	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.
别名	Spatial CEM, Geographic CEM, Spatial exact matching, CEM with spatial covariates	Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching
相关	6	6
摘要	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 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.