Causal Preference Elicitation

• URL: http://arxiv.org/abs/2602.01483v1
• Date: Sun, 01 Feb 2026 23:34:34 GMT
• Title: Causal Preference Elicitation
• Authors: Edwin V. Bonilla, He Zhao, Daniel M. Steinberg,
• Abstract summary: causal preference elicitation actively queries local edge relations to concentrate a posterior over directed acyclic graphs.<n>Experiments on synthetic graphs, protein signaling data, and a human gene benchmark show faster posterior concentration and improved recovery of directed effects under tight query budgets.
• Score: 12.331277029048437
• License: http://creativecommons.org/licenses/by/4.0/
• 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 graphs, protein signaling data, and a human gene perturbation benchmark show faster posterior concentration and improved recovery of directed effects under tight query budgets.

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