Causal Inference and Machine Learning: Mathematical Modeling, Analysis and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: 30 November 2026 | Viewed by 1280
Editors
Interests: causal inference; machine learning
Special Issue Information
Dear Colleagues,
As machine learning systems are increasingly deployed in complex real-world environments, challenges such as noisy observations, incomplete supervision, heterogeneous data sources, and adaptive behaviors call for principled mathematical frameworks to ensure reliability, interpretability, and generalization. This Special Issue will focus on the integration of causal inference and machine learning through rigorous mathematical modeling, theoretical analysis, and practical applications.
We particularly welcome contributions that develop mathematical foundations for learning under imperfect data conditions, including noisy, weak, or biased supervision, and methods that leverage causal models to distinguish spurious correlations from stable causal mechanisms. Another key theme is the combination of symbolic reasoning and logic rule mining with statistical learning, aiming to enhance explainability and robustness through structured representations. In addition, we encourage research on agent behavior modeling and strategic learning, where causal demonstrate provides insights into multi-agent interactions, feedback dynamics, and distributional shifts.
This Special Issue aims to promote interdisciplinary research, bridging statistical learning theory, causal modeling, symbolic reasoning, and agent-based analysis. Applications of interest include trustworthy AI, decision-making systems, and learning in complex interactive environments. By emphasizing mathematical modeling and analysis, this Issue aims to advance machine learning systems that are reliable, interpretable, and resilient in noisy and dynamic settings.
Topics of interest include, but are not limited to, the following:
- Mathematical modeling for causal inference in machine learning;
- Learning under noisy, weak, or biased supervision;
- Logic rule mining, symbolic reasoning, and structured learning;
- Agent behavior modeling, strategic learning, and distributional shifts;
- Applications of causal and mathematical learning frameworks.
Dr. Haotian Wang
Guest Editor
Dr. Zhiqiang Kou
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- causal inference
- mathematical modeling
- machine learning theory
- label noise
- symbolic reasoning
- agent behavior analysis
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