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| 动态倾向得分匹配× | 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≠ | 1986-2010 | 2021 |
| 提出者≠ | Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching | Callaway & Sant'Anna; Sun & Abraham |
| 类型≠ | Sequential causal matching | Causal inference / quasi-experimental |
| 开创性文献≠ | Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137. 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 PSM, sequential propensity score matching, longitudinal propensity matching, DPSM | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| 相关≠ | 6 | 4 |
| 摘要≠ | Dynamic Propensity Score Matching (DPSM) extends classic propensity score matching to settings where treatment is assigned repeatedly over time and earlier treatment choices influence later ones. It estimates the causal effect of entire treatment sequences or regime changes by constructing matched comparisons at each decision point using the full history of covariates and prior treatments. | 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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