Bandingkan metode

	Analisis Dampak Kausal Data Panel	Interrupted Time Series Data Panel
Bidang	Inferensi Kausal	Inferensi Kausal
Keluarga	Regression model	Regression model
Tahun asal	2015 (base method); panel extension mid-2010s	2000s–2010s
Pencetus	Brodersen et al. (2015); panel extension by Holtz et al. and subsequent literature	Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial)
Tipe	Bayesian structural time-series causal inference	Quasi-experimental causal inference
Sumber perintis	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.
Alias	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
Terkait	6	5
Ringkasan	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.