Causal Discovery and Its Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 25 November 2025 | Viewed by 23
Special Issue Editor
Special Issue Information
Dear Colleagues,
Causality is a fundamental notion in science and engineering. It is becoming increasingly clear that causal modeling benefits many tasks such as disease treatment, decision-making, recommender systems, adaptive/robust prediction, anomaly detection, and data generation. One focus of this Special Issue is on the fundamental problem causal discovery, i.e., how to discovery the causal structure or the underlying causal model from the observations with automated procedures? Besides causal discovery, another focus is on the applications of causal discovery, as causality-inspired machine learning in real applications scenarios can generally be well understood, so as to improve the performance, stability or explainability of the model. This Special Issue is open to methodological and applied works which can provide insightful contributions to the topic.
The sub-topics to be covered within the issue are as follows:
- Practical computational methods for causal discovery and inference
- Fundamental and testable principles to characterize causality
- Independence and conditional independence test
- Data analytics from a causal perspective
- Causality-inspired machine learning
- Causality-inspired bioinformatics
Dr. Hao Zhang
Guest Editor
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Keywords
- causal discovery
- causal inference
- independence test
- conditional independence test
- causality-inspired machine learning
- causality-inspired bioinformatics
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