Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Dynamische propensity score matching× | Dynamische Difference-in-Differences× | |
|---|---|---|
| Vakgebied | Causale inferentie | Causale inferentie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1986-2010 | 2021 |
| Grondlegger≠ | Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching | Callaway & Sant'Anna; Sun & Abraham |
| Type≠ | Sequential causal matching | Causal inference / quasi-experimental |
| Oorspronkelijke bron≠ | 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 ↗ |
| Aliassen | dynamic PSM, sequential propensity score matching, longitudinal propensity matching, DPSM | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Verwant≠ | 6 | 4 |
| Samenvatting≠ | 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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