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| 事件研究设计（因果事件研究）× | 交错双重差分法× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份 | 2021 | 2021 |
| 提出者≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) | Callaway & Sant'Anna; Sun & Abraham |
| 类型≠ | Dynamic causal panel regression | Quasi-experimental panel causal estimator |
| 开创性文献≠ | Sun, L. & Abraham, S. (2021). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Journal of Econometrics, 225(2), 175–199. DOI ↗ | Callaway, B. & Sant'Anna, P. H. C. (2021). Difference-in-Differences with Multiple Time Periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| 别名≠ | dynamic difference-in-differences, event-study DiD, dynamic treatment effects, leads-and-lags model | staggered DID, staggered adoption DID, heterogeneous treatment DID, Callaway-Sant'Anna estimator |
| 相关≠ | 5 | 4 |
| 摘要≠ | The event study design is a generalised difference-in-differences model that estimates a separate treatment-effect coefficient for each period before and after an intervention, tracing the dynamics of the effect over event time. Its modern, heterogeneity-robust form was developed by Sun & Abraham (2021) and Callaway & Sant'Anna (2021). | 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. |
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