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| Disegno di Regressione a Discontinuità Fuzzy Spaziale× | Regression Discontinuità Geografica× | |
|---|---|---|
| Campo≠ | Inferenza causale | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2015 | 2010 |
| Ideatore≠ | Keele & Titiunik (2015); fuzzy extension of geographic RDD building on Imbens & Lemieux (2008) | Melissa Dell and colleagues |
| Tipo≠ | Quasi-experimental causal inference / IV-based spatial design | Spatial quasi-experiment |
| Fonte seminale≠ | Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155. DOI ↗ | Dell, M. (2018). The persistent effects of Peru's mining mita. Econometrica, 78(6), 1863-1911. link ↗ |
| Alias≠ | Spatial Fuzzy RD, Geographic Fuzzy RDD, Spatial Fuzzy RDD, Geo-Fuzzy RD | Spatial RD, Geographic RDD |
| Correlati≠ | 5 | 3 |
| Sintesi≠ | 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. | Geographic Regression Discontinuity (GRD) is a quasi-experimental design that exploits sharp geographic boundaries—borders, policy boundaries, or natural features—to estimate causal effects. Introduced by Dell (2010) and others, it compares outcomes on either side of a boundary where treatment changes abruptly, leveraging the idea that units on opposite sides of a border are otherwise similar. This approach yields credible causal estimates for spatially localized policies, institutional changes, and natural phenomena. |
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