Salīdzināt metodes

	Daudzperiodu cēloņsakarības ietekmes analīze	Pārtraukto laika sēriju (ITS) analīze
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2015 (base); multi-period extensions 2017–present	2002
Autors	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)
Tips	Bayesian structural time-series / quasi-experimental	Quasi-experimental segmented regression
Pirmavots	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.	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.
Citi nosaukumi	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
Saistītās	6	5
Kopsavilkums	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.