Ensuring fairness in machine learning requires understanding how sensitive attributes like race or gender causally influence outcomes. Existing causal discovery (CD) methods often struggle to recover fairness-relevant pathways in the presence of noise, confounding, or data corruption. Large language models (LLMs) offer a complementary signal by leveraging semantic priors from variable metadata. We propose a hybrid LLM-guided CD framework that extends a breadth-first search strategy with active learning and dynamic scoring. Variable pairs are prioritized for querying using a composite score combining mutual information, partial correlation, and LLM confidence, enabling more efficient and robust structure discovery. To evaluate fairness sensitivity, we introduce a semi-synthetic benchmark based on the UCI Adult dataset, embedding domain-informed bias pathways alongside noise and latent confounders. We assess how well CD methods recover both global graph structure and fairness-critical paths (e.g., sex-->education-->income). Our results demonstrate that LLM-guided methods, including our active, dynamically scored variant, outperform baselines in recovering fairness-relevant structure under noisy conditions. We analyze when LLM-driven insights complement statistical dependencies and discuss implications for fairness auditing in high-stakes domains.

翻译：确保机器学习公平性需要理解种族或性别等敏感属性如何因果性地影响结果。现有的因果发现方法常在存在噪声、混杂因素或数据损坏时难以恢复与公平性相关的路径。大型语言模型通过利用变量元数据的语义先验，提供了一种互补信号。我们提出了一种混合LLM引导的因果发现框架，该框架通过主动学习和动态评分扩展了广度优先搜索策略。变量对的查询优先级通过结合互信息、偏相关和LLM置信度的复合评分来确定，从而实现更高效、更稳健的结构发现。为评估公平性敏感性，我们引入了一个基于UCI Adult数据集的半合成基准，在嵌入领域知识驱动的偏见路径的同时加入了噪声和潜在混杂因素。我们评估了因果发现方法在恢复全局图结构和公平性关键路径方面的表现。结果表明，在噪声条件下，LLM引导的方法在恢复公平性相关结构方面优于基线方法，包括我们提出的主动动态评分变体。我们分析了LLM驱动的洞察何时与统计依赖性形成互补，并讨论了在高风险领域进行公平性审计的启示。