方法对比
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| Bayesian Fuzzy Regression Discontinuity× | 局部平均处理效应（LATE / CACE）× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2001 (fuzzy RD identification); 2016 (Bayesian formulation by Chib & Jacobi) | 1994 |
| 提出者≠ | Chib & Jacobi (Bayesian formulation); Hahn, Todd & Van der Klaauw (fuzzy RD identification) | Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996) |
| 类型≠ | Bayesian causal inference / quasi-experimental design | Instrumental-variable causal estimand |
| 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 ↗ | Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475. DOI ↗ |
| 别名≠ | Bayesian Fuzzy RD, Bayesian Fuzzy RDD, Fuzzy RD with Bayesian Inference | LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE) |
| 相关 | 5 | 5 |
| 摘要≠ | Bayesian Fuzzy Regression Discontinuity (Bayesian Fuzzy RD) combines the quasi-experimental logic of fuzzy regression discontinuity design with full Bayesian inference. It estimates a local average treatment effect at a policy threshold where treatment assignment is probabilistic rather than deterministic, placing prior distributions over all unknowns and recovering a complete posterior distribution of the causal effect rather than a single point estimate. | The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compliance analysis. |
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