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2025-05-27
Special Issue on Causal Inference for Learning and Applications Submission Date: 2025-10-31 In recent years, causal inference has emerged as a critical tool for understanding cause-and-effect relationships within complex systems. By incorporating causal reasoning into machine learning, models can move beyond correlation-based learning to develop a deeper understanding of and intervention in real-world systems. This paradigm shift enables more robust, interpretable, and actionable insights, which are essential for addressing challenges in fields such as healthcare, bioinformatics, and autonomous systems.
This special issue focuses on the integration of causal inference with learning systems, highlighting its transformative potential across a wide range of fields, particularly in applications such as healthcare, bioinformatics, and decision-making systems. The special issue is aligned with the Knowledge-Based Systems (KBS) journal’s scope and aims to foster research that advances the understanding of causal reasoning in intelligent systems.
In healthcare, for example, causal inference can model patient outcomes based on medical interventions, enabling more precise treatment recommendations and improving overall patient care. Beyond healthcare, causal inference has applications in areas such as genomics, precision medicine, robotics, and complex decision-making, where understanding causal relationships is vital for designing effective strategies.
Guest editors:
Prof. Huanhuan Chen (Executive Guest Editor)
University of Science and Technology of China, Hefei, China
Email: [email protected]
Prof. Chunyan Miao
Nanyang Technological University, Singapore
Email: [email protected]
Prof. Peter Tino
University of Birmingham, Birmingham, UK
Email: [email protected]
Prof. Mengjie Zhang
Victoria University of Wellington, Wellington, New Zealand
Email: [email protected]
Prof. Xin Yao
Lingnan University, Hong Kong, China
Email: [email protected]
Special issue information:
This special issue aims to highlight state-of-the-art research on causal inference and its applications in learning systems and other critical domains. We invite submissions that address, but are not limited to, the following topics:
Theories and methods for causal inference in learning systems
Learning causal structures in complex systems
Learning causal structures using evolutionary algorithms
Integration of counterfactual reasoning in machine learning
Applications of causal inference in healthcare, including precision medicine and disease modeling
Optimization under uncertainty using causal models
Explainable AI via causal reasoning
Real-world applications of causal inference in genomics, bioinformatics, robotics, and personalized medicine
Manuscript submission information:
Important Dates:
Submission Open Date: April 1, 2025
Manuscript Submission Deadline: October 31, 2025
Completion of Review and Revision Process: March 31, 2026
Final Notification: April 30, 2026
Contributed papers must be submitted via the Knowledge-Based Systems online submission system (Editorial Manager?): Please select the article type “VSI: Causal Inference for Learning and Applications” when submitting the manuscript online.
Please refer to the Guide for Authors to prepare your manuscript.
For any further information, the authors may contact the Guest Editors.
Keywords:
Causal Inference, counterfactual reasoning, explainable learning
