方法对比
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| 空间倾向得分匹配× | 空间合成控制法× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2000s | 2003–2010s |
| 提出者≠ | 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 |
| 类型≠ | Quasi-experimental matching estimator | Quasi-experimental causal inference |
| 开创性文献≠ | 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. DOI ↗ | Abadie, A., & Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. American Economic Review, 93(1), 113-132. DOI ↗ |
| 别名 | Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching | spatial SCM, geographic synthetic control, spatial SC, spatial counterfactual control |
| 相关 | 6 | 6 |
| 摘要≠ | 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. |
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