方法对比
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| 交错双重差分法× | 回归断点设计 (Regression Discontinuity Design, RDD)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2021 | 2008 |
| 提出者≠ | Callaway & Sant'Anna; Sun & Abraham | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) |
| 类型≠ | Quasi-experimental panel causal estimator | Quasi-experimental causal design |
| 开创性文献≠ | Callaway, B. & Sant'Anna, P. H. C. (2021). Difference-in-Differences with Multiple Time Periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ |
| 别名≠ | staggered DID, staggered adoption DID, heterogeneous treatment DID, Callaway-Sant'Anna estimator | RDD, regression discontinuity design, sharp RDD, fuzzy RDD |
| 相关≠ | 4 | 5 |
| 摘要≠ | Staggered Difference-in-Differences is a generalisation of DID for panel designs in which treatment is rolled out to different groups at different times. Introduced in the modern form by Callaway and Sant'Anna (2021) and Sun and Abraham (2021), it corrects the bias that classical two-way fixed-effects (TWFE) estimators suffer when treatment effects are heterogeneous across cohorts and over time. | Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold. |
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