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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.01483 |
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| _version_ | 1866911416008572928 |
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| author | Bonilla, Edwin V. Zhao, He Steinberg, Daniel M. |
| author_facet | Bonilla, Edwin V. Zhao, He Steinberg, Daniel M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01483 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Causal Preference Elicitation Bonilla, Edwin V. Zhao, He Steinberg, Daniel M. Machine Learning Artificial Intelligence Methodology 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. |
| title | Causal Preference Elicitation |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2602.01483 |