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| 공간 엔트로피 균형× | 공간 성향 점수 매칭× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2010s | 2000s |
| 창시자≠ | Extension of Hainmueller (2012) entropy balancing to spatial settings; spatial adaptations developed in geographic epidemiology and spatial econometrics literature | Extension of Rosenbaum & Rubin (1983) PSM to spatial settings; spatial adaptation developed in applied econometrics and epidemiology literature from the 2000s onward |
| 유형≠ | Quasi-experimental reweighting | Quasi-experimental matching estimator |
| 원전≠ | 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 ↗ |
| 별칭≠ | spatial EB, geographically-weighted entropy balancing, spatial reweighting | Spatial PSM, Geospatial PSM, Spatially-adjusted propensity score matching, Geographic propensity score matching |
| 관련 | 6 | 6 |
| 요약≠ | 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. | Spatial Propensity Score Matching (Spatial PSM) extends the classic propensity score matching framework to settings where units are embedded in geographic space and treatment assignment or outcomes may be spatially correlated. By incorporating spatial covariates and adjacency structure into the propensity model and matching procedure, it produces causal estimates that account for geographic confounding and spillover effects. |
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