Quantitative causality, causality-guided scientific discovery, and causal machine learning
CoRR(2024)
摘要
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 Niño Modoki, the forecasting of an extreme drought
in China, among others.
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