arXiv:2602.01483v2 Announce Type: replace Abstract: We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over directed acyclic graphs (DAGs). From any black-box observational posterior, we model noisy expert judgments with a three-way likelihood over edge existence and direction. Posterior inference uses a flexible particle approximation, and queries are selected by an efficient expected information gain criterion on the expert's categorical response. Experiments on synthetic
The paper introduces a Bayesian framework for integrating expert knowledge into causal discovery, leveraging active learning techniques to improve model accuracy and efficiency.
This development can significantly enhance the robustness and interpretability of AI systems, particularly in critical domains where understanding causality is paramount for decision-making.
The ability to actively query human experts during causal discovery allows for more data-efficient and domain-relevant model building, moving beyond purely observational data.
- · AI researchers
- · Healthcare sector
- · Finance sector
- · Industrial automation
- · Developers of opaque AI systems
Improved performance and reliability of AI systems in complex, real-world applications requiring causal understanding.
Increased adoption of explainable AI and causal inference methods across various industries, fostering greater trust in AI.
Potentially accelerates the development of more sophisticated, human-aligned AI agents capable of nuanced reasoning and interaction.
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Read at arXiv cs.LG