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| Conception d'étude événementielle (Causal Event Study)× | Différences-en-Différences Échelonnées× | |
|---|---|---|
| Domaine | Inférence causale | Inférence causale |
| Famille | Regression model | Regression model |
| Année d'origine | 2021 | 2021 |
| Auteur d'origine≠ | Sun & Abraham (2021); Callaway & Sant'Anna (2021) | Callaway & Sant'Anna; Sun & Abraham |
| Type≠ | Dynamic causal panel regression | Quasi-experimental panel causal estimator |
| Source fondatrice≠ | Sun, L. & Abraham, S. (2021). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Journal of Econometrics, 225(2), 175–199. DOI ↗ | Callaway, B. & Sant'Anna, P. H. C. (2021). Difference-in-Differences with Multiple Time Periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| Alias≠ | dynamic difference-in-differences, event-study DiD, dynamic treatment effects, leads-and-lags model | staggered DID, staggered adoption DID, heterogeneous treatment DID, Callaway-Sant'Anna estimator |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | The event study design is a generalised difference-in-differences model that estimates a separate treatment-effect coefficient for each period before and after an intervention, tracing the dynamics of the effect over event time. Its modern, heterogeneity-robust form was developed by Sun & Abraham (2021) and Callaway & Sant'Anna (2021). | Staggered Difference-in-Differences is a generalisation of DID for panel designs in which treatment is rolled out to different groups at different times. Introduced in the modern form by Callaway and Sant'Anna (2021) and Sun and Abraham (2021), it corrects the bias that classical two-way fixed-effects (TWFE) estimators suffer when treatment effects are heterogeneous across cohorts and over time. |
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