Quantitative causality, causality-guided scientific discovery, and causal machine learning

• URL: http://arxiv.org/abs/2402.13427v1
• Date: Tue, 20 Feb 2024 23:38:46 GMT
• Title: Quantitative causality, causality-guided scientific discovery, and causal machine learning
• Authors: X. San Liang, Dake Chen and Renhe Zhang
• Abstract summary: This note provides a brief review of the decade-long effort to establish a rigorous formalism of causality analysis. It includes a list of major theoretical results, a sketch of the causal deep learning framework, and some representative real-world applications in geoscience.
• Score: 0.23020018305241333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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