Módszerek összehasonlítása

	Machine learning-augmented causal impact analysis	A különbség-különbségek (Diff-in-Diff) módszer
Tudományterület	Oksági következtetés	Ökonometria
Módszercsalád	Regression model	Regression model
Keletkezés éve	2015-2018	1994
Megalkotó	Brodersen et al. (foundational BSTS framework, 2015); Chernozhukov et al. (double ML augmentation, 2018)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Típus	Quasi-experimental causal inference with ML	Causal inference / panel regression
Alapmű	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
Alternatív nevek	ML-augmented causal impact, ML-CausalImpact, machine learning causal impact, ML-augmented BSTS	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Kapcsolódó	6	5
Összefoglaló	Machine learning-augmented causal impact analysis combines quasi-experimental counterfactual reasoning with flexible ML prediction models to estimate the causal effect of an intervention on a time series outcome. Building on Brodersen et al.'s Bayesian structural time series (BSTS) framework and extended by double/debiased ML methods, it constructs a synthetic counterfactual from donor covariates and infers the treatment effect as the gap between observed and predicted post-intervention outcomes.	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.