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.

因果分析对于可解释的深度学习和泛化提供了有前景的途径，通过将因果性纳入人工智能算法，并利用气象可预测性初次构建严格的因果分析形式体系，在过去的 18 年中，成功解决了以往因果性分析中模糊、非数量化、计算效率低等挑战，并通过广泛应用在大气海洋科学以及其他学科领域，如量子力学、神经科学、金融经济学等，带来了科学发现。本文简要回顾了这十年来的探索工作，包括重要的理论结果列表、因果深度学习框架的概述以及与地球科学相关的一些实际应用，例如关于全球变暖人为原因、El Niño Modoki 的年代预测、中国极端干旱的预测等。