Causal Inference and Causal AI: Machine Learning Meets Information Theory

Dear Colleagues,

In several fields, such as statistics, machine learning, and quantum information theory, inferring the latent structure is a crucial task. This involves uncovering hidden variables or underlying patterns that can explain the observed data. There are two key steps in this process: model specification and causal inference. Model specification involves selecting a probabilistic model that can capture the relationship between the observed and latent variables. Causal inference requires computing the posterior distribution of the latent variables based on the observed data. Successful applications in computer science include topic modeling, collaborative filtering in recommendation systems and automated theorem proving. In recent years, causality has also begun to play a critical role in addressing decision-making issues, such as Explainable Artificial Intelligence (AI) and causal AI, due to its ability to facilitate generalization and robustness. Causal AI refers to the use of machine learning algorithms and techniques to identify causal relationships and make predictions based on them. This facilitates the use of Explainable AI in designing AI systems that can provide clear and understandable explanations for their decisions and actions. Causal AI has a wide range of applications in various fields, such as healthcare, finance, and marketing. For example, in healthcare, causal AI can be used to identify the causes of diseases and develop effective treatments. In finance, it can be used to identify the causes of market fluctuations and predict future trends. Information–theoretic approaches provide a unique set of tools that can expand the range of traditional methods for causal inference and discovery problems in various applications. These include the use of entropy regularization, directed information, the minimum information principle, minimum entropy couplings, and the information bottleneck principle to analyze causal structures of deep neural networks. Causal inference and causal AI thus rely on information theory to quantify the performance of machine learning techniques to make explainable predictions or decisions. For this Special Issue, we are inviting the submission of research presenting novel machine learning and information–theoretic approaches to causal learning in real-world applications, including, but not limited to: causal inference, model interpretation, graphical models, belief propagation and message-passing algorithms, Explainable AI based on the information–theoretic perspective, generative AI and causal AI, and emerging machine learning applications based on Large Language Models.

Dr. Chee Wei Tan
Dr. Siu-Wai Ho
Guest Editors

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