Bayesian Networks and Causal Discovery

Dear Colleagues,

A bedrock topic in artificial intelligence is the discovery of the precise causal representations underlying data. Therefore, causal discovery is important for understanding data’s underlying mechanisms. As a probabilistic graphical model, a BN is a directed acyclic graph (DAG) in which each node represents a random variable and the directed edges between nodes represent the dependencies between variables. These relationships are further quantified by a set of conditional probability distributions. BNs have been applied to explore causality in various fields, such as fault detection, medical support, reliability analysis, and so on.

We hope that this Special Issue will become a forum for researchers in the field of Bayesian networks and causal discovery. Therefore, we are seeking unpublished original papers and comprehensive reviews focused on (but not limited to) the following research areas:

• Bayesian network modeling, including structure learning, parameter learning, and inference algorithms.
• Recent, popular continuous optimization algorithms in causal discovery, e.g., graph neural networks, reinforcement learning, etc.
• The combination of Bayes and neural networks, e.g., Bayesian neural networks and deep Bayesian learning.
• Novel causal models to represent causality.
• The application of BNs and causal discovery, e.g., expert systems, reliability analysis, etc.
• Causal discovery under confounding factors, e.g., noise, faithfulness, sufficiency, knowledge, and small datasets.

Prof. Dr. Xiaoguang Gao
Guest Editor

Dr. Zidong Wang
Guest Editor Assistant

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