Causal discovery (CD) is an important component of many scientific applications, yet most techniques produce unreliable point estimates that often contradict expert knowledge. To mitigate this, recent research has focused on ex-ante incorporation of background knowledge into the CD process, typically under an unrealistic causal sufficiency assumption. When probing experts is costly (e.g., hidden behind expensive LLM APIs), however, ex-post model refinement that maximizes query utility is preferable. Also, when independent experts provide conflicting but better-than-random feedback, a principled aggregation method is required. In this context, we introduce the first CD algorithm that enables (i) distributional inference over ancestral graphs (AGs), which represent causal systems under latent confounding, and (ii) integration of both ex-ante and uncertain ex-post expert knowledge. Briefly, our method is a diversity-seeking reinforcement learning algorithm, termed Ancestral GFlowNet (AGFN), whose policy we iteratively refine based on a Bayesian model of the noisy expert feedback. Importantly, we prove convergence to the true AG given sufficiently accurate responses. Through validation on synthetic and realistic datasets using simulated humans and LLMs, we show AGFN is competitive with or superior to strong baselines in terms of structural Hamming distance and Bayesian Information Criterion.

翻译：因果发现（CD）是许多科学应用的重要组成部分，然而大多数技术产生不可靠的点估计，常常与专家知识相矛盾。为缓解此问题，近期研究聚焦于将背景知识以事前方式纳入CD过程，通常基于不现实的因果充分性假设。然而，当向专家征询成本高昂时（例如，隐藏于昂贵的LLM API之后），最大化查询效用的事后模型精炼更为可取。此外，当独立专家提供相互冲突但优于随机猜测的反馈时，需要一种原则性的聚合方法。在此背景下，我们提出了首个CD算法，该算法能够（i）对祖先图（AG）进行分布推断（AG表示存在潜在混杂的因果系统），并（ii）整合事前与不确定的事后专家知识。简言之，我们的方法是一种寻求多样性的强化学习算法，称为祖先GFlowNet（AGFN），其策略基于噪声专家反馈的贝叶斯模型进行迭代精炼。重要的是，我们证明了在专家反馈足够准确的条件下，算法能收敛至真实的AG。通过在合成与真实数据集上使用模拟人类和LLM进行验证，我们表明AGFN在结构汉明距离和贝叶斯信息准则方面与强基线方法相比具有竞争力或更优。