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| Monijaksoinen kausaalivaikutusten analyysi× | Monijaksoinen keskeytetty aikasarja-analyysi× | |
|---|---|---|
| Tieteenala | Kausaalipäättely | Kausaalipäättely |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2015 (base); multi-period extensions 2017–present | 2000s-2015 |
| Kehittäjä≠ | Brodersen, Gallusser, Koehler, Remy & Scott (Google); extended to multi-period settings by subsequent applied work | Extended from segmented regression / ITS tradition; multi-break formalization developed across epidemiology and health policy literature (2000s-2010s) |
| Tyyppi≠ | Bayesian structural time-series / quasi-experimental | Quasi-experimental time series regression |
| Alkuperäislähde≠ | 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 ↗ | Kontopantelis, E., Doran, T., Springate, D. A., Buchan, I., & Reeves, D. (2015). Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ, 350, h2750. DOI ↗ |
| Rinnakkaisnimet | multi-period CausalImpact, staggered causal impact, repeated-period causal impact, multi-wave CausalImpact | multi-period ITS, multiple-interruption ITS, segmented time series with multiple breakpoints, MITS |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | 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. | Multi-period Interrupted Time Series (MITS) extends the classic ITS framework to settings where two or more interventions occur at known time points within the same series. By fitting a segmented regression with multiple breakpoints, MITS estimates the level change and slope change attributable to each intervention while controlling for the underlying secular trend and for the effects of earlier interruptions. |
| ScholarGateAineisto ↗ |
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