在因果图不完整时，用变量聚类实现更公平的预测。
Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables
- 用变量聚类替代精细因果图，降低建模难度。
- 通过最小化干预分布的最大差异，提升公平性与准确性的平衡。
- 适合因果知识有限但需公平决策的场景。
算法对个体的决策不仅需要准确，还需在性别、种族等敏感属性上公平。因果公平性符合法律要求，但现有方法常假设已知完整的因果图，这在实践中难以实现。本文提出一种学习框架，利用变量聚类层面的因果图（而非单变量级别）来实现干预公平性。基于该聚类因果图识别出可能的调整聚类集，通过最小化这些集合间干预分布的最坏情况差异，训练预测模型。为此，我们设计了一种计算高效的巴氏中心核最大均值差异（MMD），其复杂度随敏感属性取值数量增长缓慢。大量实验表明，本框架在公平性与准确性之间取得了更优平衡，尤其适用于因果图信息不全的场景。
原文摘要 · Abstract (English)
Algorithmic decisions about individuals require predictions that are not only accurate but also fair with respect to sensitive attributes such as gender and race. Causal notions of fairness align with legal requirements, yet many methods assume access to detailed knowledge of the underlying causal graph, which is a demanding assumption in practice. We propose a learning framework that achieves interventional fairness by leveraging a causal graph over \textit{clusters of variables}, which is substantially easier to estimate than a variable-level graph. With possible \textit{adjustment cluster sets} identified from such a cluster causal graph, our framework trains a prediction model by reducing the worst-case discrepancy between interventional distributions across these sets. To this end, we develop a computationally efficient barycenter kernel maximum mean discrepancy (MMD) that scales favorably with the number of sensitive attribute values. Extensive experiments show that our framework strikes a better balance between fairness and accuracy than existing approaches, highlighting its effectiveness under limited causal graph knowledge.
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