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| 정책 평가를 위한 퍼지 회귀 불연속점× | 정책 평가 회귀 불연속 설계× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2001 | 1960; policy evaluation applications widespread from 2000s |
| 창시자≠ | Hahn, Todd & Van der Klaauw | Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010) |
| 유형≠ | Quasi-experimental / local IV estimator | Quasi-experimental causal design |
| 원전≠ | 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 ↗ | Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355. DOI ↗ |
| 별칭 | Fuzzy RDD, Fuzzy RD, Fuzzy Regression Discontinuity, Imperfect Compliance RDD | Policy RDD, RD design in policy evaluation, regression discontinuity policy analysis, RDD policy impact |
| 관련 | 5 | 5 |
| 요약≠ | 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. | 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. |
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