  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.
