标题： 因果偏好获取 显示英文标题

标题： Causal Preference Elicitation

摘要： 我们提出因果偏好获取，这是一种用于专家在回路中的因果发现的贝叶斯框架，它主动查询局部边关系以集中后验于有向无环图（DAGs）。 从任何黑盒观察后验出发，我们通过边存在性和方向的三重似然来建模噪声专家判断。 后验推断使用灵活的粒子近似，查询通过专家分类响应上的高效期望信息增益准则进行选择。 在合成图、蛋白质信号数据和人类基因扰动基准上的实验表明，在有限的查询预算下，后验集中速度更快，定向效应的恢复效果更好。 显示英文摘要

摘要： 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.