ScholarGate
Avustaja

Vertaile menetelmiä

Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.

Bayesiläinen kausaalivaikutusanalyysi×Aikasarjojen katkosanalyysi (Interrupted Time Series, ITS)×
TieteenalaKausaalipäättelyKausaalipäättely
MenetelmäperheRegression modelRegression model
Syntyvuosi20152002
KehittäjäBrodersen, Gallusser, Koehler, Remy & Scott (Google)Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
TyyppiBayesian causal inference / time seriesQuasi-experimental segmented regression
AlkuperäislähdeBrodersen, 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 ↗
RinnakkaisnimetCausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysisITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Liittyvät45
TiivistelmäBayesian 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.
ScholarGateAineisto
  1. v1
  2. 2 Lähteet
  3. PUBLISHED
  1. v1
  2. 2 Lähteet
  3. PUBLISHED

Siirry hakuun Lataa diat

ScholarGateVertaile menetelmiä: Bayesian Causal Impact Analysis · Interrupted Time Series. Haettu 2026-06-18 osoitteesta https://scholargate.app/fi/compare