方法对比
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| 动态合成控制法× | 动态双重差分× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010 | 2021 |
| 提出者≠ | Abadie, Diamond & Hainmueller (2010); dynamic extensions by Abadie (2021) and others | Callaway & Sant'Anna; Sun & Abraham |
| 类型≠ | Comparative case study / counterfactual estimation | Causal inference / quasi-experimental |
| 开创性文献≠ | Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505. 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 SCM, Time-varying synthetic control, Multi-period synthetic control, DSC | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| 相关≠ | 5 | 4 |
| 摘要≠ | The Dynamic Synthetic Control Method extends the classic synthetic control framework to evaluate treatments that unfold over multiple periods or change in intensity over time. It constructs a weighted combination of untreated units that matches the treated unit in pre-treatment outcomes, then traces the full time path of treatment effects period by period after the intervention — capturing not just an average effect but how the effect evolves dynamically. | Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time. |
| ScholarGate数据集 ↗ |
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