Uporedite metode

	Analiza kauzalnog uticaja panel podataka	Panel Data Interrupted Time Series
Oblast	Kauzalno zaključivanje	Kauzalno zaključivanje
Porodica	Regression model	Regression model
Godina nastanka	2015 (base method); panel extension mid-2010s	2000s–2010s
Tvorac	Brodersen et al. (2015); panel extension by Holtz et al. and subsequent literature	Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial)
Tip	Bayesian structural time-series causal inference	Quasi-experimental causal inference
Temeljni izvor	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.	Lopez Bernal, J., 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.
Drugi nazivi	Panel CausalImpact, multi-unit causal impact, panel BSTS causal inference, panel structural time-series causal analysis	panel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series
Srodne	6	5
Sažetak	Panel data causal impact analysis extends the Bayesian structural time-series approach of Brodersen et al. (2015) to multi-unit panel settings, estimating the counterfactual for several treated units simultaneously using control units as a donor pool. It produces credible intervals for the causal effect at each post-intervention time point, aggregated across units and periods.	Panel Data Interrupted Time Series (panel ITS) is a quasi-experimental method that estimates the causal effect of an intervention using repeated observations from multiple units over time. By exploiting variation across both units and time periods, it provides stronger causal identification than single-unit ITS, detecting changes in the level and slope of the outcome trajectory immediately following a clearly dated intervention.