Сравнение на методи

	Машинно обучение-подобрена прекъсната времева серия	Метод на разликите в разликите (Difference-in-Differences, DiD)
Област	Причинно-следствено заключение	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2014-2015	1994
Създател	Brodersen et al. (2015); Varian (2014) — foundational ML-for-causal-inference literature	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Тип	Quasi-experimental causal inference with ML counterfactual	Causal inference / panel regression
Основополагащ източник	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
Други названия	ML-ITS, ML-augmented ITS, machine learning ITS, causal ML interrupted time series	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Свързани	6	5
Резюме	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.