Causal Preference Elicitation
Machine Learning
2026-02-03 v1 Artificial Intelligence
Methodology
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.
Cite
@article{arxiv.2602.01483,
title = {Causal Preference Elicitation},
author = {Edwin V. Bonilla and He Zhao and Daniel M. Steinberg},
journal= {arXiv preprint arXiv:2602.01483},
year = {2026}
}