Methoden vergleichen

	Maschinelles Lernen-gestützte unterbrochene Zeitreihenanalyse	Analyse von unterbrochenen Zeitreihen (Interrupted Time Series, ITS)
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	2014-2015	2002
Urheber	Brodersen et al. (2015); Varian (2014) — foundational ML-for-causal-inference literature	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Typ	Quasi-experimental causal inference with ML counterfactual	Quasi-experimental segmented regression
Wegweisende Quelle	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.
Aliasnamen	ML-ITS, ML-augmented ITS, machine learning ITS, causal ML interrupted time series	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Verwandt	6	5
Zusammenfassung	Machine Learning-Augmented Interrupted Time Series (ML-ITS) estimates the causal effect of a discrete intervention by training a machine learning model on pre-intervention time series data, projecting a counterfactual trajectory into the post-intervention period, and measuring the gap between observed and predicted outcomes. It extends classical ITS by replacing parametric trend assumptions with flexible ML estimators such as gradient boosting, random forests, or Bayesian structural time-series models.	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.