Overview
This editorial highlights a significant opportunity for researchers in the field of artificial intelligence and machine learning. A special issue titled “Causal AI: Integrating Causality and Machine Learning for Robust Intelligent Systems” is being organized by Frontiers in Artificial Intelligence and Frontiers in Big Data. This initiative aims to explore the intersection of causality and AI, encouraging innovative contributions that enhance the robustness and trustworthiness of machine learning models.
Background & Relevance
Causal AI is a rapidly evolving area that combines principles of causality with machine learning techniques. This integration is crucial for developing intelligent systems that can make reliable predictions and decisions based on causal relationships. The ability to understand and leverage causality can lead to more generalizable models, which is essential in various applications, from healthcare to autonomous systems. As the demand for trustworthy AI systems grows, the relevance of this research area becomes increasingly significant.
Key Details
- Manuscript Summary Submission Deadline: 15 November 2025
- Manuscript Submission Deadline: 27 February 2026
- Research Topics: Causal inference, causal discovery, counterfactual reasoning, causal representation learning, individualised treatment effect estimation
- Links: Research Topic on Frontiers
Eligibility & Participation
This call for papers is open to researchers, practitioners, and academics involved in the fields of AI and machine learning. Contributions are welcomed from those who are exploring innovative methodologies and applications in causal AI.
Submission or Application Guidelines
To participate, authors should prepare their manuscripts according to the guidelines provided by the hosting journals. The process involves:
1. Preparing a manuscript summary by the specified deadline.
2. Submitting the full manuscript by the final submission deadline.
3. Ensuring that contributions align with the themes of causality and machine learning.
More Information
Causal AI is becoming increasingly important as it provides a framework for understanding the underlying mechanisms of data. This research area not only enhances the interpretability of machine learning models but also improves their applicability in real-world scenarios. By integrating causal reasoning into AI, researchers can develop systems that are not only intelligent but also capable of making informed decisions based on causal insights.
Conclusion
Researchers are encouraged to contribute to this special issue, as it represents a vital step towards advancing the field of Causal AI. By submitting your work, you can help shape the future of robust intelligent systems. Explore this opportunity to share your insights and innovations in this emerging area of research.
Category: CFP & Deadlines
Tags: causal ai, machine learning, causal inference, robust systems, counterfactual reasoning, causal discovery, individualised treatment effects, frontiers in artificial intelligence, frontiers in big data, research topic
