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| Többperiódusú kauzális hatásanalízis× | Megszakított Idősor (ITS) Elemzés× | |
|---|---|---|
| Tudományterület | Oksági következtetés | Oksági következtetés |
| Módszercsalád | Regression model | Regression model |
| Keletkezés éve≠ | 2015 (base); multi-period extensions 2017–present | 2002 |
| Megalkotó≠ | Brodersen, Gallusser, Koehler, Remy & Scott (Google); extended to multi-period settings by subsequent applied work | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Típus≠ | Bayesian structural time-series / quasi-experimental | Quasi-experimental segmented regression |
| Alapmű≠ | 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. 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 ↗ |
| Alternatív nevek≠ | multi-period CausalImpact, staggered causal impact, repeated-period causal impact, multi-wave CausalImpact | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Kapcsolódó≠ | 6 | 5 |
| Összefoglaló≠ | Multi-period Causal Impact Analysis extends the Bayesian structural time-series framework of Brodersen et al. (2015) to settings where an intervention occurs across multiple distinct periods, is applied at staggered times to different units, or where researchers wish to evaluate cumulative and period-specific effects within a single unified model. It builds a synthetic counterfactual from control covariates and projects it across each intervention window to quantify causal effects. | 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. |
| ScholarGateAdatkészlet ↗ |
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