Comparer des méthodes
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| Différences-en-Différences Échelonnées× | La régression par discontinuité (RDD)× | |
|---|---|---|
| Domaine | Inférence causale | Inférence causale |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2021 | 2008 |
| Auteur d'origine≠ | Callaway & Sant'Anna; Sun & Abraham | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) |
| Type≠ | Quasi-experimental panel causal estimator | Quasi-experimental causal design |
| Source fondatrice≠ | Callaway, B. & Sant'Anna, P. H. C. (2021). Difference-in-Differences with Multiple Time Periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ |
| Alias≠ | staggered DID, staggered adoption DID, heterogeneous treatment DID, Callaway-Sant'Anna estimator | RDD, regression discontinuity design, sharp RDD, fuzzy RDD |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | 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. | Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold. |
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