方法对比
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| 模糊断点回归政策评估 (Fuzzy Regression Discontinuity for Policy Evaluation)× | 因果推断的工具变量（IV）方法× | |
|---|---|---|
| 领域≠ | 因果推断 | 卫生经济学 |
| 方法族≠ | Regression model | Process / pipeline |
| 起源年份≠ | 2001 | 1990s (modern applications) |
| 提出者≠ | Hahn, Todd & Van der Klaauw | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| 类型≠ | Quasi-experimental / local IV estimator | Method |
| 开创性文献≠ | 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 ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| 别名 | Fuzzy RDD, Fuzzy RD, Fuzzy Regression Discontinuity, Imperfect Compliance RDD | IV, two-stage least squares, TSLS, causal estimation |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. | Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes. |
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