Порівняння методів

	Просторове зіставлення за показником схильності	Просторовий метод синтетичного контролю
Галузь	Причинно-наслідковий висновок	Причинно-наслідковий висновок
Родина	Regression model	Regression model
Рік появи	2000s	2003–2010s
Автор методу	Extension of Rosenbaum & Rubin (1983) PSM to spatial settings; spatial adaptation developed in applied econometrics and epidemiology literature from the 2000s onward	Abadie & Gardeazabal (2003); extended to spatial settings by subsequent applied econometric work
Тип	Quasi-experimental matching estimator	Quasi-experimental causal inference
Основоположне джерело	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.	Abadie, A., & Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. American Economic Review, 93(1), 113-132.
Інші назви	Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching	spatial SCM, geographic synthetic control, spatial SC, spatial counterfactual control
Пов'язані	6	6
Підсумок	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.	The Spatial Synthetic Control Method adapts the classic synthetic control framework to settings where treated and donor units are defined by geographic location. By constructing a weighted combination of spatially proximate or comparable control regions, the method estimates what would have happened to a treated area absent the intervention, while explicitly accounting for geographic spillovers, spatial autocorrelation, and contiguity among units.