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| 空间合成控制法× | 倾向得分匹配× | |
|---|---|---|
| 领域≠ | 因果推断 | 研究统计学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 2003–2010s | 1983 |
| 提出者≠ | Abadie & Gardeazabal (2003); extended to spatial settings by subsequent applied econometric work | Paul Rosenbaum and Donald Rubin |
| 类型≠ | Quasi-experimental causal inference | Method |
| 开创性文献≠ | 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 ↗ | 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 ↗ |
| 别名≠ | spatial SCM, geographic synthetic control, spatial SC, spatial counterfactual control | PSM, propensity score weighting, covariate balance |
| 相关≠ | 6 | 3 |
| 摘要≠ | 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. | Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias. |
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