Linganisha mbinu

	Uchambuzi wa Athari Husababishi wa Tathmini ya Sera	Mchanganuo wa Mijadala ya Wakati wa Sera Iliyoingiliwa
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2015	1975 (intervention analysis); 2000s–2010s (policy evaluation framing)
Mwanzilishi	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)
Aina	Bayesian counterfactual / time-series	Quasi-experimental causal design
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.	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.
Majina mbadala	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
Zinazohusiana	6	4
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.	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.