Fairness is a critical objective in policy design and algorithmic decision-making. Identifying the causal pathways of unfairness requires knowledge of the underlying structural causal model, which may be incomplete or unavailable. This limits the practicality of causal fairness analysis in complex or low-knowledge domains. To mitigate this practicality gap, we advocate for developing efficient causal discovery methods for fairness applications. To this end, we introduce local discovery for direct discrimination (LD3): a polynomial-time algorithm that recovers structural evidence of direct discrimination. LD3 performs a linear number of conditional independence tests with respect to variable set size. Moreover, we propose a graphical criterion for identifying the weighted controlled direct effect (CDE), a qualitative measure of direct discrimination. We prove that this criterion is satisfied by the knowledge returned by LD3, increasing the accessibility of the weighted CDE as a causal fairness measure. Taking liver transplant allocation as a case study, we highlight the potential impact of LD3 for modeling fairness in complex decision systems. Results on real-world data demonstrate more plausible causal relations than baselines, which took 197x to 5870x longer to execute.

翻译：公平性是政策设计与算法决策中的关键目标。识别不公平的因果路径需要了解底层的结构因果模型，而该模型可能不完整或不可得。这限制了因果公平性分析在复杂或低知识领域中的实用性。为缓解这一实践差距，我们主张开发适用于公平性应用的高效因果发现方法。为此，我们提出直接歧视的局部发现（LD3）：一种可在多项式时间内恢复直接歧视结构证据的算法。LD3执行的条件独立性检验次数相对于变量集规模呈线性增长。此外，我们提出了一种用于识别加权受控直接效应（CDE）的图准则，该效应是直接歧视的定量度量。我们证明了LD3返回的知识满足该准则，从而提高了加权CDE作为因果公平性度量的可及性。以肝移植分配为案例研究，我们强调了LD3在复杂决策系统中建模公平性的潜在影响。真实世界数据上的结果表明，相比基线方法，LD3能发现更合理的因果关系，而基线方法的执行时间长达LD3的197倍至5870倍。