Linganisha mbinu

	Uchambuzi wa Athari Husababishi wa Tathmini ya Sera	Uchambuzi wa Athari ya Kifahili
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2015	2015
Mwanzilishi	Brodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contexts	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
Aina	Bayesian counterfactual / time-series	Bayesian causal inference / counterfactual forecasting
Chanzo asilia	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.
Majina mbadala	policy causal impact, BSTS policy evaluation, Bayesian policy impact assessment, CIA policy evaluation	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
Zinazohusiana	6	5
Muhtasari	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.