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：我们提出因果偏好启发，一种专家在环的贝叶斯因果发现框架，通过主动查询局部边关系来集中有向无环图的后验分布。该框架从任意黑盒观测后验出发，采用三态似然函数对边的存在性与方向性建模专家带噪声的判断。后验推断采用灵活的粒子近似方法，查询选择则基于专家分类响应的高效期望信息增益准则。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集中速度，并提升有向效应的恢复效果。