Causal Preference Elicitation

Edwin V. Bonilla; He Zhao; Daniel M. Steinberg

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arXiv · 2602.01483

Causal Preference Elicitation

Edwin V. Bonilla·He Zhao·Daniel M. Steinberg

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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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Edwin V. Bonilla, He Zhao, Daniel M. Steinberg. 2026-02-01. Causal Preference Elicitation. https://arxiv.org/abs/2602.01483

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