Quantitative causality, causality-guided scientific discovery, and causal machine learning
<|reference_start|>Quantitative causality, causality-guided scientific discovery, and causal machine learning: It has been said, arguably, that causality analysis should pave a promising way to interpretable deep learning and generalization. Incorporation of causality into artificial intelligence (AI) algorithms, however, is challenged with its vagueness, non-quantitiveness, computational inefficiency, etc. During the past 18 years, these challenges have been essentially resolved, with the establishment of a rigorous formalism of causality analysis initially motivated from atmospheric predictability. This not only opens a new field in the atmosphere-ocean science, namely, information flow, but also has led to scientific discoveries in other disciplines, such as quantum mechanics, neuroscience, financial economics, etc., through various applications. This note provides a brief review of the decade-long effort, including a list of major theoretical results, a sketch of the causal deep learning framework, and some representative real-world applications in geoscience pertaining to this journal, such as those on the anthropogenic cause of global warming, the decadal prediction of El Ni\~no Modoki, the forecasting of an extreme drought in China, among others.<|reference_end|>
@article{liang2024quantitative,
    title={Quantitative causality, causality-guided scientific discovery, and
  causal machine learning},
    author={X. San Liang, Dake Chen and Renhe Zhang},
    journal={arXiv preprint arXiv:2402.13427},
    year={2024},
    doi={10.34133/olar.0026},
    archivePrefix={arXiv},
    eprint={2402.13427},
    primaryClass={cs.AI physics.data-an}
}