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| Policy Evaluation Regression Discontinuity Design× | Fuzzy regressionsdiskontinuitetsdesign× | |
|---|---|---|
| Ämnesområde | Kausal inferens | Kausal inferens |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 1960; policy evaluation applications widespread from 2000s | 2001 |
| Upphovsperson≠ | Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010) | Hahn, Todd & van der Klaauw |
| Typ≠ | Quasi-experimental causal design | Quasi-experimental causal inference |
| Ursprungskälla≠ | Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355. 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 | Policy RDD, RD design in policy evaluation, regression discontinuity policy analysis, RDD policy impact | Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | Policy Evaluation Regression Discontinuity Design (Policy RDD) exploits a known eligibility threshold in a policy rule to estimate the causal effect of that policy on outcomes. Units just below the cutoff serve as a credible comparison group for units just above it, making RDD one of the most transparent quasi-experimental strategies for assessing what a policy actually achieves. | 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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