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| 공간 성향 점수 가중치× | 지리 가중 회귀 분석 (Geographically Weighted Regression, GWR)× | |
|---|---|---|
| 분야≠ | 인과추론 | 공간분석 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2000s–2010s | 2002 |
| 창시자≠ | Extended from Hirano, Imbens & Ridder (2003) IPTW with spatial adaptations by Keele, Titiunik and others in geographically structured causal designs | Fotheringham, Brunsdon & Charlton |
| 유형≠ | Quasi-experimental / causal inference | Local spatial regression |
| 원전≠ | 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 |
| 별칭 | 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) |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. |
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