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| Phân tích độ nhạy không gian cho tính nhân quả× | Hồi quy Trọng số Địa lý (GWR)× | |
|---|---|---|
| Lĩnh vực≠ | Suy luận nhân quả | Phân tích không gian |
| Họ | Regression model | Regression model |
| Năm ra đời≠ | 1988–2021 (developed progressively) | 2002 |
| Người khởi xướng≠ | Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks | Fotheringham, Brunsdon & Charlton |
| Loại≠ | Sensitivity / robustness analysis | Local spatial regression |
| Công trình gốc≠ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. ISBN: 978-9024737322 | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 |
| Tên gọi khác | spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) |
| Liên quan≠ | 6 | 5 |
| Tóm tắt≠ | Spatial sensitivity analysis for causality systematically tests whether a causal estimate derived from georeferenced data holds up as spatial structure, spillovers, and the choice of spatial weights matrix are varied. Because nearby units often share unmeasured confounders — soil quality, local infrastructure, neighbourhood norms — a naive regression may yield biased causal estimates. This method reveals how fragile or robust a claimed causal effect is to alternative spatial specifications. | 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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