方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 空间倾向得分加权× | 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≠ | 2000s–2010s | 2002 |
| 提出者≠ | Extended from Hirano, Imbens & Ridder (2003) IPTW with spatial adaptations by Keele, Titiunik and others in geographically structured causal designs | Fotheringham, Brunsdon & Charlton |
| 类型≠ | Quasi-experimental / causal inference | Local spatial regression |
| 开创性文献≠ | Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155. 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 PSW, geographically weighted propensity score weighting, spatial IPTW, spatially adjusted inverse probability weighting | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| 相关≠ | 6 | 5 |
| 摘要≠ | Spatial propensity score weighting extends inverse probability of treatment weighting (IPTW) to settings where units are geographically located and treatment assignment may depend on spatial factors such as location, neighborhood characteristics, or spatial clustering. By incorporating spatial covariates into the propensity score model and adjusting standard errors for spatial autocorrelation, it produces more credible causal estimates from observational geographic data. | 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. |
| ScholarGate数据集 ↗ |
|
|