方法对比
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| 空间熵平衡× | 倾向得分加权法 (PSW / IPW)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010s | 1983 (propensity score); 2003 (efficient IPW estimator) |
| 提出者≠ | Extension of Hainmueller (2012) entropy balancing to spatial settings; spatial adaptations developed in geographic epidemiology and spatial econometrics literature | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| 类型≠ | Quasi-experimental reweighting | Causal inference / reweighting |
| 开创性文献≠ | Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46. 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 EB, geographically-weighted entropy balancing, spatial reweighting | PSW, inverse probability weighting, IPW, propensity-based weighting |
| 相关 | 6 | 6 |
| 摘要≠ | Spatial entropy balancing extends standard entropy balancing to observational settings where units are embedded in geographic space, incorporating spatial structure into the reweighting process so that balance is achieved while respecting spatial proximity, clustering, or spillover dependencies between units. | Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003). |
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