Krahasoni metodat

	Analiza e Ndikimit Shkakësor	Analiza e Serive Kohore të Ndërprera (ITS)
Fusha	Inferenca kauzale	Inferenca kauzale
Familja	Regression model	Regression model
Viti i origjinës	2015	2002
Krijuesi	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Lloji	Bayesian causal inference / counterfactual forecasting	Quasi-experimental segmented regression
Burimi themelues	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.
Emërtime të tjera	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Të lidhura	5	5
Përmbledhja	Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals.	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.