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	Policy Evaluation Causal Impact Analysis	Policyutvärdering med avbruten tidsserie
Ämnesområde	Kausal inferens	Kausal inferens
Familj	Regression model	Regression model
Ursprungsår	2015	1975 (intervention analysis); 2000s–2010s (policy evaluation framing)
Upphovsperson	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)
Typ	Bayesian counterfactual / time-series	Quasi-experimental causal design
Ursprungskälla	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
Närliggande	6	4
Sammanfattning	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.