方法对比
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| 多期合成控制法× | 多期双重差分法（交错DiD）× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010-2021 | 2021 |
| 提出者≠ | Abadie, Diamond & Hainmueller (2010); extended to multi-period settings by Abadie (2021) and Ben-Michael et al. (2021) | Callaway & Sant'Anna; Goodman-Bacon |
| 类型≠ | Quasi-experimental causal inference | Causal inference / panel regression |
| 开创性文献≠ | Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature, 59(2), 391-425. 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 | multi-period SCM, extended synthetic control, synthetic control with multiple treatment periods, staggered synthetic control | staggered DiD, multi-period DiD, staggered difference-in-differences, heterogeneous timing DiD |
| 相关 | 5 | 5 |
| 摘要≠ | The multi-period synthetic control method extends the classic synthetic control framework to settings where treatment occurs across several distinct periods or where the researcher needs to track causal effects over a prolonged post-treatment window. It constructs a weighted combination of untreated units that reproduces the treated unit's pre-treatment trajectory, then uses that synthetic counterfactual across all post-treatment periods to estimate time-varying treatment effects. | Multi-period Difference-in-Differences extends the classic two-period DiD framework to settings where units adopt treatment at different points in time. Formalised by Callaway and Sant'Anna (2021) and Goodman-Bacon (2021), it decomposes the overall treatment effect into group-time average treatment effects and addresses the bias that arises when conventional two-way fixed-effects regressions are applied to staggered adoption designs. |
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