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 Niño Modoki, the forecasting of an extreme drought in China, among others. Keywords: Causality, Liang-Kleeman information flow, Causal artificial intelligence, Fuzzy cognitive map, Interpretability, Frobenius-Perron operator, Weather/Climate forecasting 1. Introduction Causality analysis is a fundamental problem in scientific research, as commented by Einstein in 1953 in response to a question on the status quo of science in China at that time (cf. the historical record in Hu, 2005).The recent rush in artificial intelligence (AI) has stimulated enormous interest in causal inference, partly due to the realization that it may take the field to the next level to approach human intelligence (see Pearl, 2018; Bengio, 2019; Schölkopf, 2022). In the fields pertaining to this journal, assessment of the cause-effect relations between dynamic events makes a natural objective for the corresponding researches.