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| Fuzzy Regression Discontinuity för Policyutvärdering× | Fuzzy regressionsdiskontinuitetsdesign× | |
|---|---|---|
| Ämnesområde | Kausal inferens | Kausal inferens |
| Familj | Regression model | Regression model |
| Ursprungsår | 2001 | 2001 |
| Upphovsperson | Hahn, Todd & Van der Klaauw | Hahn, Todd & van der Klaauw |
| Typ≠ | Quasi-experimental / local IV estimator | Quasi-experimental causal inference |
| Ursprungskälla | 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 ↗ | 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 | Fuzzy RDD, Fuzzy RD, Fuzzy Regression Discontinuity, Imperfect Compliance RDD | Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates the causal effect of a policy when eligibility is determined by crossing a threshold on a continuous score, but actual take-up or compliance is imperfect. Developed formally by Hahn, Todd, and Van der Klaauw (2001), it uses the threshold as an instrumental variable to recover a Local Average Treatment Effect (LATE) among compliers near the cutoff. | 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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