Comparer des méthodes

	Évaluation de politiques Analyse d'impact causal	Évaluation de politiques par séries chronologiques interrompues
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2015	1975 (intervention analysis); 2000s–2010s (policy evaluation framing)
Auteur d'origine	Brodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contexts	Box & Tiao (1975); popularised for policy by Shadish, Cook & Campbell (2002) and Bernal et al. (2017)
Type	Bayesian counterfactual / time-series	Quasi-experimental causal design
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.	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.
Alias	policy causal impact, BSTS policy evaluation, Bayesian policy impact assessment, CIA policy evaluation	ITS for policy evaluation, policy ITS, segmented regression for policy, policy impact ITS
Apparentées	6	4
Résumé	Policy Evaluation Causal Impact Analysis applies the Bayesian structural time-series (BSTS) framework of Brodersen et al. (2015) to estimate the causal effect of a policy intervention on aggregate outcomes. By constructing a synthetic counterfactual from pre-policy data and control covariates, it asks: what would have happened had the policy not been enacted? The difference between observed and predicted post-policy outcomes is the estimated policy effect.	Interrupted Time Series (ITS) for policy evaluation uses routinely collected aggregate time-series data to estimate the causal impact of a policy change. A segmented regression model splits the series at a known intervention date, estimating both an immediate level shift and a change in trend attributable to the policy — without requiring a randomised control group.