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Linganisha mbinu

Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.

Uchambuzi wa Athari Husababishi wa Tathmini ya Sera×Uchanganuzi wa Mfululizo wa Wakati Uliokatizwa (ITS)×
NyanjaUhitimisho wa KisababishiUhitimisho wa Kisababishi
FamiliaRegression modelRegression model
Mwaka wa asili20152002
MwanzilishiBrodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contextsWagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
AinaBayesian counterfactual / time-seriesQuasi-experimental segmented regression
Chanzo asiliaBrodersen, 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. DOI ↗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. DOI ↗
Majina mbadalapolicy causal impact, BSTS policy evaluation, Bayesian policy impact assessment, CIA policy evaluationITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Zinazohusiana65
MuhtasariPolicy 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 analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.
ScholarGateSeti ya data
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  1. v1
  2. 2 Vyanzo
  3. PUBLISHED

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ScholarGateLinganisha mbinu: Policy Evaluation Causal Impact Analysis · Interrupted Time Series. Imepatikana 2026-06-19 kutoka https://scholargate.app/sw/compare