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| 多期回归断点设计× | 模糊回归断点设计× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010s–2020s | 2001 |
| 提出者≠ | Cattaneo, Idrobo & Titiunik (foundations); extended by multiple authors for repeated-period settings | Hahn, Todd & van der Klaauw |
| 类型 | Quasi-experimental causal inference | Quasi-experimental causal inference |
| 开创性文献≠ | Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2020). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press. 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 ↗ |
| 别名 | multi-wave RD, repeated RDD, dynamic RD, multi-cutoff RDD | Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD |
| 相关≠ | 3 | 5 |
| 摘要≠ | Multi-period Regression Discontinuity Design extends the classic RDD to settings where a cutoff-based treatment is applied in multiple waves, across repeated time periods, or with varying thresholds. By pooling or comparing period-specific discontinuity estimates, researchers gain statistical precision and can examine how causal effects evolve or persist over time. | 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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