Causal Preference Elicitation

Edwin V. Bonilla, He Zhao, Daniel M. Steinberg
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:9094-9128, 2026.

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 this Paper

BibTeX
@InProceedings{pmlr-v306-bonilla26a, title = {Causal Preference Elicitation}, author = {Bonilla, Edwin V. and Zhao, He and Steinberg, Daniel M.}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {9094--9128}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/bonilla26a/bonilla26a.pdf}, url = {https://proceedings.mlr.press/v306/bonilla26a.html}, 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.} }
Endnote
%0 Conference Paper %T Causal Preference Elicitation %A Edwin V. Bonilla %A He Zhao %A Daniel M. Steinberg %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-bonilla26a %I PMLR %P 9094--9128 %U https://proceedings.mlr.press/v306/bonilla26a.html %V 306 %X 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.
APA
Bonilla, E.V., Zhao, H. & Steinberg, D.M.. (2026). Causal Preference Elicitation. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:9094-9128 Available from https://proceedings.mlr.press/v306/bonilla26a.html.

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