Comparer des méthodes

	Conception d'étude d'événements augmentée par l'apprentissage automatique	Différence-en-différences dynamique
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010s–2020s	2021
Auteur d'origine	Chernozhukov et al. (double/debiased ML foundation); applied to event studies in subsequent econometrics literature	Callaway & Sant'Anna; Sun & Abraham
Type	Quasi-experimental / causal inference	Causal inference / quasi-experimental
Source fondatrice	Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Alias	ML-augmented event study, high-dimensional event study, DML event study, causal ML event study	Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD
Apparentées	3	4
Résumé	Machine learning-augmented event study design combines the standard event study framework — which traces outcome dynamics around a treatment date — with ML-based methods such as double/debiased machine learning (DML) or regularized regression to handle high-dimensional covariates, improve confounder control, and produce valid causal estimates when the covariate space is too large for conventional regression to manage reliably.	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.