Porównaj metody

	Uczenie maszynowe wspomagane przerywanymi szeregami czasowymi	Metoda różnic w różnicach (Diff-in-Diff)
Dziedzina	Wnioskowanie przyczynowe	Ekonometria
Rodzina	Regression model	Regression model
Rok powstania	2014-2015	1994
Twórca	Brodersen et al. (2015); Varian (2014) — foundational ML-for-causal-inference literature	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Typ	Quasi-experimental causal inference with ML counterfactual	Causal inference / panel regression
Źródło pierwotne	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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Inne nazwy	ML-ITS, ML-augmented ITS, machine learning ITS, causal ML interrupted time series	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Pokrewne	6	5
Podsumowanie	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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.