Causal Graphical Models and Their Applications

Dear Colleagues,

The concept of causality deals with the regularities found in a given environment that have stronger than probabilistic (or associative) relations, in the sense that a causal relation allows for evaluating a change in the consequence given a change in the cause. Recently, there has been an increasing interest in causal models, in particular causal graphical models, since several cognitive processes, such as causal reasoning, can be best represented as graphical models. Causal models, in contrast with traditional associative models, provide a more powerful representation that can be used for reasoning about interventions and counterfactuals. They can also help us to build more transparent and robust intelligent systems and provide explanations. A challenging problem is to create these models using observational data, known as causal discovery, an active research area. These models are being applied in different fields, such as biology, medicine, and economics, among others.

The objective of this Special Issue is 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
Prof. Dr. David Danks
Guest Editors

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• causal graphical models
• causal reasoning
• causal discovery
• applications of causal models