Information-Theoretic Causal Inference and Discovery
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 1865
Special Issue Editor
Special Issue Information
Dear Colleagues,
Causal inference is one of the main focus areas in artificial intelligence (AI) and machine learning (ML). Causality has received significant interest in ML in the recent years in part due to its utility for generalization and robustness. It is also central for tackling decision-making problems such as bandit problems, reinforcement learning, policy, or experimental design. Information-theoretic assumptions and techniques open new avenues for causality research ranging from discovery to inference. Some examples of the success of information theory in causal inference are the use of directed information, minimum entropy couplings and common entropy for bivariate causal discovery, the use of the information bottleneck principle with applications in the generalization of machine learning models, and analyzing causal structures of deep neural networks with information theory, among others.
This Special Issue focuses on bringing information theory and causality together to expand the scope of current causal reasoning algorithms. The expected contributions range from the introduction of new assumptions to pave the way for better analyses to addressing a causal question in a well-studied setting using a novel information-theoretic approach. Some applications include causal graph discovery and the identification of interventional or counterfactual distributions from data.
Dr. Murat Kocaoglu
Guest Editor
Manuscript Submission Information
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Keywords
- causal graphs
- causal discovery
- causal inference
- experimental design
- information theory
- entropy
