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	Policy Evaluation Causal Impact Analysis	Analys av kausal påverkan
Ämnesområde	Kausal inferens	Kausal inferens
Familj	Regression model	Regression model
Ursprungsår	2015	2015
Upphovsperson	Brodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contexts	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
Typ	Bayesian counterfactual / time-series	Bayesian causal inference / counterfactual forecasting
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.	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.
Alias	policy causal impact, BSTS policy evaluation, Bayesian policy impact assessment, CIA policy evaluation	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
Närliggande	6	5
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.	Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals.