用专家反馈加速因果图发现，高效缩小可能结构范围。
Causal Preference Elicitation
- 基于贝叶斯框架主动询问局部边关系，引导专家判断
- 在有限提问次数下，更快收敛且更准识别因果方向
- 适合需专家参与的因果推断任务，如生物通路研究
我们提出因果偏好获取，一种专家参与的贝叶斯因果发现框架，通过主动查询局部边的存在与方向，集中后验分布于有向无环图（DAG）上。针对任意黑箱观测后验，我们以三类可能性建模噪声专家判断，涵盖边存在性与方向。后验推断采用灵活的粒子近似，查询选择基于对专家分类响应的期望信息增益准则。在合成图、蛋白质信号数据及人类基因扰动基准测试中，该方法在严格查询预算下实现更快的后验集中，并显著提升有向效应的恢复准确率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。