Causal Graphical Models and Their Applications, 2nd Edition
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: 28 February 2026 | Viewed by 19
Special Issue Editor
Interests: probabilistic reasoning in artificial intelligence; computer vision and image processing; service robots; causal graphical models; causal discovery
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The concept of causality concerns the regularities found in a given environment that have stronger than probabilistic (or associative) relations, in the sense that a causal relation enables the evaluation of a change in the consequence given a change in the cause. Recently, there has been an increasing interest in causal models, particularly causal graphical models, since several cognitive processes, such as causal reasoning, can be best represented as graphical models. Unlike traditional associative models, causal models provide a more powerful representation that can be used for reasoning about interventions and counterfactuals. They can also contribute to the development of more transparent and robust intelligent systems. A challenge in this field is the creation of these models using observational data, known as causal discovery, which is an active research area. These models are being applied in different fields such as biology, medicine, and economics, among others.
This Special Issue aims to present recent advances in causal reasoning and causal discovery based on causal graphical models, including novel applications in different domains.
Dr. Luis Enrique Sucar
Guest Editor
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Keywords
- causal graphical models
- causal reasoning
- causal discovery
- applications of causal models
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Related Special Issue
- Causal Graphical Models and Their Applications in Entropy (7 articles)
