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| Disegno di Regressione a Discontinuità Fuzzy Spaziale× | Regression Discontinuity Design Fuzzy× | |
|---|---|---|
| Campo | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2015 | 2001 |
| Ideatore≠ | Keele & Titiunik (2015); fuzzy extension of geographic RDD building on Imbens & Lemieux (2008) | Hahn, Todd & van der Klaauw |
| Tipo≠ | Quasi-experimental causal inference / IV-based spatial design | Quasi-experimental causal inference |
| Fonte seminale≠ | Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155. DOI ↗ | Hahn, J., Todd, P., & van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209. DOI ↗ |
| Alias | Spatial Fuzzy RD, Geographic Fuzzy RDD, Spatial Fuzzy RDD, Geo-Fuzzy RD | Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD |
| Correlati | 5 | 5 |
| 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. | Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near the threshold. |
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