Causal Inference in Statistics and AI
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 29 October 2025 | Viewed by 131
Special Issue Editors
Interests: machine learning and statistical decision making; causal inference; multi-agent systems and game theory
2. Department of Data Science and AI, Monash University, Clayton, VIC 3800, Australia
Interests: machine learning; discrete non-parametric and Bayesian statistics; Bayesian algorithms; probabilistic deep learning; probabilistic text mining and natural language processing
Special Issue Information
Dear Colleagues,
Causal inference plays a fundamental role in modern data science, enabling researchers to uncover cause-effect relationships beyond mere associations. With increasing access to complex, high-dimensional, and dynamic datasets, recent advances in causal inference have been driven by methodological innovations, including machine learning-based causal discovery, counterfactual reasoning, and causal effect estimation in non-traditional settings such as networks and time-series data.
This Special Issue aims to bring together cutting-edge research in causal inference in statistics and AI, covering both theoretical advancements and practical applications. We welcome contributions on a wide range of topics, including but not limited to novel methods for causal discovery and effect estimation, causal inference in high-dimensional and structured data, statistical aspects of causal inference, robustness, entropy and sensitivity analysis in causal models, and their applications in healthcare, economics, social sciences, and artificial intelligence.
This issue seeks to provide a platform for statisticians, AI and computer scientists, and domain experts to advance the state of causal reasoning and its applications. We invite original research, methodological contributions, and review articles that push the boundaries of causal inference in theory and practice.
Dr. Jalal Etesami
Prof. Dr. Wray L. Buntine
Guest Editors
Manuscript Submission Information
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Keywords
- causal discovery
- causal effect estimation and counterfactual inference
- causal representation learning
- high-dimensional causal inference
- fairness, transparency, and explainability in AI
- time-series and dynamic causal inference
- statistical aspects of causal inference
- sensitivity analysis and robustness in causal models
- foundational theories of causal inference
- applications in healthcare, economics, and AI
- information theoretic approaches, Granger causality, transfer entropy
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