ScholarGate
Asistent

Usporedite metode

Pregledajte odabrane metode jednu uz drugu; retci koji se razlikuju su istaknuti.

Bayesian Causal Impact Analysis×Analiza prekinutih vremenskih serija (ITS)×
PodručjeUzročno zaključivanjeUzročno zaključivanje
ObiteljRegression modelRegression model
Godina nastanka20152002
TvoracBrodersen, Gallusser, Koehler, Remy & Scott (Google)Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
VrstaBayesian causal inference / time seriesQuasi-experimental segmented regression
Temeljni izvorBrodersen, 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 ↗
Drugi naziviCausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysisITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Srodne45
SažetakBayesian Causal Impact Analysis uses a Bayesian structural time series (BSTS) model to estimate the causal effect of an intervention on a time series outcome. Developed by Brodersen and colleagues at Google in 2015, it builds a probabilistic counterfactual — what the series would have looked like without the intervention — from pre-intervention data and optional control covariates, then compares it with the observed post-intervention values to produce a fully Bayesian posterior over the causal 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.
ScholarGateSkup podataka
  1. v1
  2. 2 Izvori
  3. PUBLISHED
  1. v1
  2. 2 Izvori
  3. PUBLISHED

Idi na pretraživanje Preuzmi prezentaciju

ScholarGateUsporedite metode: Bayesian Causal Impact Analysis · Interrupted Time Series. Preuzeto 2026-06-18 s https://scholargate.app/hr/compare