对比三种因果发现算法在医疗公平性与效用上的表现，强调需细粒度分析路径影响。
Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare
- 构建代理真实图谱，评估算法对因果结构和路径公平性的恢复能力。
- 彼得-克拉克算法在结构恢复上最优，快速因果推断在心衰数据中效用最高。
- 射血分数贡献3.37个百分点的间接效应，影响不同算法的公平-效用平衡。
健康数据中的因果发现因缺乏真实因果图而难以评估。我们与专家合作构建了代理真实图谱，为阿尔茨海默病和心力衰竭临床记录的合成数据建立了基准。评估了Peter-Clark、Greedy Equivalence Search和Fast Causal Inference算法在结构恢复和路径特定公平性分解方面的表现，超越了综合公平性评分。在合成数据上，Peter-Clark实现最佳结构恢复；在心衰数据上，Fast Causal Inference达到最高效用。路径特定分析显示，射血分数在真实图中对间接效应贡献3.37个百分点。这一差异导致各算法在公平性-效用比上的显著差异。研究强调，在临床应用中部署因果发现时，需采用图感知的公平性评估与细粒度路径分析。
原文摘要 · Abstract (English)
Causal discovery in health data faces evaluation challenges when ground truth is unknown. We address this by collaborating with experts to construct proxy ground-truth graphs, establishing benchmarks for synthetic Alzheimer's disease and heart failure clinical records data. We evaluate the Peter-Clark, Greedy Equivalence Search, and Fast Causal Inference algorithms on structural recovery and path-specific fairness decomposition, going beyond composite fairness scores. On synthetic data, Peter-Clark achieved the best structural recovery. On heart failure data, Fast Causal Inference achieved the highest utility. For path-specific effects, ejection fraction contributed 3.37 percentage points to the indirect effect in the ground truth. These differences drove variations in the fairness-utility ratio across algorithms. Our results highlight the need for graph-aware fairness evaluation and fine-grained path-specific analysis when deploying causal discovery in clinical applications.
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