Comparer des méthodes

	Méthode robuste de contrôle synthétique	Différences-en-différences robustes
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2021	2021-2023
Auteur d'origine	Cattaneo, Feng & Titiunik (2021); building on Abadie, Diamond & Hainmueller (2010)	Callaway & Sant'Anna; Sun & Abraham; Roth et al. (synthesised 2021-2023)
Type	Quasi-experimental causal inference	Causal inference / panel regression
Source fondatrice	Cattaneo, M. D., Feng, Y., & Titiunik, R. (2021). Prediction Intervals for Synthetic Control Methods. Journal of the American Statistical Association, 116(536), 1865-1880.	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.
Alias	Robust SCM, Inference-robust synthetic control, Synthetic control with valid inference, SCM with prediction intervals	robust DiD, heterogeneity-robust DiD, staggered DiD, disaggregated ATT DiD
Apparentées	5	5
Résumé	The robust synthetic control method extends the classic synthetic control estimator by providing statistically valid uncertainty quantification and inference. Developed by Cattaneo, Feng and Titiunik (2021), it addresses a core limitation of the original approach — the lack of formal prediction intervals — making causal conclusions more defensible when only a single treated unit is observed.	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.