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.

翻译：我们提出因果偏好学习，这是一个贝叶斯框架，用于专家参与的因果发现。该方法通过主动查询局部边关系，以集中有向无环图（DAGs）的后验分布。基于任意黑箱观测后验，我们使用关于边存在性与方向的三元似然函数对含噪专家判断进行建模。后验推理采用灵活的粒子近似，并通过关于专家分类响应的有效期望信息增益准则选择查询。在合成图、蛋白质信号数据及人类基因扰动基准上的实验表明，该方法在严格查询预算下能实现更快的后验收敛，并显著提升有向效应恢复效果。