Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making. In this work, we explore how expert knowledge can be used to improve the data-driven identification of causal graphs, beyond Markov equivalence classes. In doing so, we consider a setting where we can query an expert about the orientation of causal relationships between variables, but where the expert may provide erroneous information. We propose strategies for amending such expert knowledge based on consistency properties, e.g., acyclicity and conditional independencies in the equivalence class. We then report a case study, on real data, where a large language model is used as an imperfect expert.

翻译：理解系统背后的因果关系是准确决策的基本前提。在这项工作中，我们探索如何利用专家知识改善基于数据的因果图识别，超越马尔可夫等价类。在此过程中，我们考虑了一种情境：可以向专家查询变量之间因果关系的方向，但专家可能提供错误信息。我们基于一致性属性（例如，等价类中的无环性和条件独立性）提出了修正此类专家知识的策略。最后，我们报告了一项基于真实数据的案例研究，其中大型语言模型被用作不完美专家。