Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Динамічне зіставлення за показником схильності× | Динамічний метод різниці різниць× | |
|---|---|---|
| Галузь | Причинно-наслідковий висновок | Причинно-наслідковий висновок |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1986-2010 | 2021 |
| Автор методу≠ | Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching | Callaway & Sant'Anna; Sun & Abraham |
| Тип≠ | Sequential causal matching | Causal inference / quasi-experimental |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | dynamic PSM, sequential propensity score matching, longitudinal propensity matching, DPSM | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Пов'язані≠ | 6 | 4 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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