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| 多期双重稳健估计× | 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≠ | 1994-2021 | 2021 |
| 提出者≠ | Robins, Rotnitzky, and Zhao; extended by Bang & Robins (2005) and Callaway & Sant'Anna (2021) | Callaway & Sant'Anna; Sun & Abraham |
| 类型≠ | Semiparametric causal estimator | Causal inference / quasi-experimental |
| 开创性文献≠ | Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973. 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 | longitudinal DR estimation, multi-period DR, multi-wave doubly robust, sequential doubly robust estimation | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| 相关≠ | 6 | 4 |
| 摘要≠ | Multi-period doubly robust (DR) estimation extends the classic doubly robust approach to longitudinal settings with multiple treatment periods and time points. It combines an outcome regression model and a propensity score model for each period, retaining consistency of the causal effect estimate as long as at least one of the two models is correctly specified at every time point. | 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. |
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