Comparar métodos

	Desenho de Estudo de Eventos Aumentado por Aprendizado de Máquina	Diferenças em Diferenças Dinâmicas
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2010s–2020s	2021
Autor original	Chernozhukov et al. (double/debiased ML foundation); applied to event studies in subsequent econometrics literature	Callaway & Sant'Anna; Sun & Abraham
Tipo	Quasi-experimental / causal inference	Causal inference / quasi-experimental
Fonte seminal	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.
Outros nomes	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
Relacionados	3	4
Resumo	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.