Comparer des méthodes

	Analyse robuste des séries chronologiques interrompues	Différences-en-différences robustes
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010s	2021-2023
Auteur d'origine	Bernal, Cummins & Gasparrini; Linden (robust extensions)	Callaway & Sant'Anna; Sun & Abraham; Roth et al. (synthesised 2021-2023)
Type	Quasi-experimental segmented regression with robust inference	Causal inference / panel regression
Source fondatrice	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Alias	robust ITS, outlier-robust ITS, robust segmented regression, robust ITSA	robust DiD, heterogeneity-robust DiD, staggered DiD, disaggregated ATT DiD
Apparentées	5	5
Résumé	Robust Interrupted Time Series Analysis is a quasi-experimental method that estimates the causal effect of a policy or intervention on an aggregate outcome over time, using segmented regression fitted with outlier-resistant or heteroskedasticity-consistent standard errors. It is widely used in health services research and public-health evaluation when the time series contains influential observations, non-constant variance, or mild autocorrelation.	Robust Difference-in-Differences is a family of modern DiD estimators designed to remain valid when treatment timing is staggered across units and treatment effects are heterogeneous over time or across groups. Classical two-way fixed-effects (TWFE) DiD can be severely biased in such settings; robust variants estimate group-time average treatment effects (ATTs) separately and then aggregate them in a theoretically sound way.