Comparer des méthodes

	Analyse d'impact causal robuste	Méthode du Contrôle Synthétique (MCS)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2015	2003–2010
Auteur d'origine	Brodersen, Gallusser, Koehler, Remy & Scott (foundational CausalImpact framework)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Type	Bayesian causal inference with robustness validation	Quasi-experimental causal inference
Source fondatrice	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
Alias	robust CausalImpact, sensitivity-augmented causal impact, causal impact with robustness checks, robust BSTS causal inference	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Apparentées	5	4
Résumé	Robust Causal Impact Analysis extends the Bayesian structural time-series CausalImpact framework (Brodersen et al., 2015) by embedding systematic robustness checks — in-time placebo tests, in-space placebo controls, covariate sensitivity analysis, and prior sensitivity assessments — to verify that a detected intervention effect is genuine and not an artifact of model choices or coincidental data patterns.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.