Applications of Entropy in Causality Analysis

Dear Colleagues, 

In the analysis of large-scale systems, causality has become an important concept through which to describe the relationships between phenomena, events, or elements. In particular, in abnormal situations, a local and small fault can propagate through material or information paths to a wide range, leading to a global fault, a significant failure, or even an accident. It is therefore vital to capture the causal relationship in addition to the correlation or association that is easily obtained from time series data. Based on this causality information, one can trace the origin and predict the consequence, which is beneficial for abnormal situation analysis. Causality can be described and captured through statistical analysis. For linear relationships, Granger causality is widely used. For other complex cases, information-theoretic methods have shown their advantages. Entropy-based techniques, such as transfer entropy, directed transfer entropy, transfer zero-entropy, and their variants or extensions, have been developed and have proven effective in many applications.

Hence, this Special Issue, entitled “Applications of Entropy in Causality Analysis”, welcomes theoretical or application submissions reporting original research on the development and application of entropy-based techniques to quantify, characterize, or model causality through time series. We are also happy to receive reviews and commentaries aligned with the vision of this Special Issue. Specifically, this Special Issue will accept unpublished original papers and comprehensive reviews focused on (but not restricted to) the following research areas:

• Entropy-based approaches for causality analysis
• Data-driven methods for causality analysis
• Process knowledge or model-based connectivity and causality analysis
• Parametric or non-parametric models for cause–effect relations
• Causality inference for root cause analysis
• Applications of causality analysis in (but not limited to) the manufacturing industry, information technology, biological sciences, and social sciences

Dr. Fan Yang
Dr. Wenkai Hu
Guest Editors

Manuscript Submission Information

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