Econometrics and machine learning share core predictive and causal methodologies
Econometrics and machine learning increasingly overlap, with hybrid methodologies combining econometric causal frameworks with machine learning's predictive power.
The retrieved papers provide strong evidence that econometrics and machine learning share and increasingly integrate both predictive and causal methodologies (such as in causal machine learning, hybrid time-series modeling, and personalized causal inference in econometrics). There are no refuting papers.
Khelfaoui I, Wang W, Meskher H, Shehata AI, El Basuini MF, Abouelenein MF, Degha HE, Alhoshy M, Teiba II, Mahmoud SS. Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools.. 2025. https://doi.org/10.3389/fmicb.2025.1691503
Discusses how econometric tools and machine learning are integrated to validate causal relationships and handle predictive modeling.
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Martín-Álvarez JM, Galiano A, Hoz BV, Lyalkov S. The long-term impact of Spain's 2010 Anti-Smoking Law: A counterfactual and prospective time-series analysis.. 2026. https://doi.org/10.3934/publichealth.2026011
Demonstrates the combination of econometric and machine learning models in a hybrid framework for predictive time-series and counterfactual analysis.
Selvarama Lakshmi. Beyond the Average: Machine Learning for Personalized Causal Inference in Econometrics. 2024. https://doi.org/10.53469/jrse.2024.06(11).13
Shows how machine learning algorithms are applied within econometrics to move beyond average treatment effects toward personalized causal inference.
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