方法对比
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| 空间模糊回归不连续设计× | 空间回归不连续设计 (Spatial RDD)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2015 | 2010s |
| 提出者≠ | Keele & Titiunik (2015); fuzzy extension of geographic RDD building on Imbens & Lemieux (2008) | Popularized by Dell (2010); formalized for geographic boundaries by Keele & Titiunik (2015) |
| 类型≠ | Quasi-experimental causal inference / IV-based spatial design | Quasi-experimental causal inference |
| 开创性文献≠ | Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155. DOI ↗ | Dell, M. (2010). The Persistent Effects of Peru's Mining Mita. Econometrica, 78(6), 1863-1903. DOI ↗ |
| 别名 | Spatial Fuzzy RD, Geographic Fuzzy RDD, Spatial Fuzzy RDD, Geo-Fuzzy RD | Spatial RDD, Geographic RDD, Border RD Design, Geographic Discontinuity Design |
| 相关≠ | 5 | 4 |
| 摘要≠ | Spatial Fuzzy Regression Discontinuity Design (Spatial Fuzzy RDD) estimates a local average treatment effect when a geographic boundary determines treatment eligibility but some units on either side of the boundary fail to comply with their assigned status. It combines the spatial running-variable logic of geographic RDD with the instrumental-variable correction for imperfect compliance used in fuzzy RDD. | Spatial Regression Discontinuity Design uses a geographic or administrative boundary as the threshold that assigns units to treatment. Observations just inside one side of the boundary are compared with those just outside it, exploiting the near-random variation in treatment status near the cutoff to recover a local causal effect. The approach is widely used in economics, political science, and public health when policies or institutions change sharply at a border. |
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