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Байєсівський аналіз причинного впливу×Аналіз перерваних часових рядів (ITS)×
ГалузьПричинно-наслідковий висновокПричинно-наслідковий висновок
РодинаRegression modelRegression model
Рік появи20152002
Автор методуBrodersen, Gallusser, Koehler, Remy & Scott (Google)Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
ТипBayesian causal inference / time seriesQuasi-experimental segmented regression
Основоположне джерелоBrodersen, 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 ↗
Інші назвиCausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysisITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Пов'язані45
ПідсумокBayesian 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.
ScholarGateНабір даних
  1. v1
  2. 2 Джерела
  3. PUBLISHED
  1. v1
  2. 2 Джерела
  3. PUBLISHED

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ScholarGateПорівняння методів: Bayesian Causal Impact Analysis · Interrupted Time Series. Отримано 2026-06-18 з https://scholargate.app/uk/compare