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| 空间双重稳健估计× | 地理加权回归 (GWR)× | |
|---|---|---|
| 领域≠ | 因果推断 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010s–2020s | 2002 |
| 提出者≠ | Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature | Fotheringham, Brunsdon & Charlton |
| 类型≠ | Semiparametric causal estimator | Local spatial regression |
| 开创性文献≠ | Papadogeorgou, G., Mealli, F., & Zigler, C. M. (2019). Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics, 75(3), 778-787. DOI ↗ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| 别名 | Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| 相关 | 5 | 5 |
| 摘要≠ | Spatial doubly robust estimation is a semiparametric causal inference method that combines propensity score weighting with outcome regression modeling — providing protection against misspecification of either component — while explicitly accounting for spatial autocorrelation among units. It extends the classical augmented inverse probability weighting (AIPW) estimator to settings where treatment assignment and outcomes are geographically clustered or spatially dependent. | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. |
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