Local Causal Discovery for Structural Evidence of Direct Discrimination
<|reference_start|>Local Causal Discovery for Structural Evidence of Direct Discrimination: Identifying the causal pathways of unfairness is a critical objective in improving policy design and algorithmic decision-making. Prior work in causal fairness analysis often requires knowledge of the causal graph, hindering practical applications in complex or low-knowledge domains. Moreover, global discovery methods that learn causal structure from data can result in unstable performance with finite samples, potentially leading to contradictory fairness conclusions. To mitigate these issues, we introduce local discovery for direct discrimination (LD3): a method that uncovers structural evidence of direct discrimination by identifying the causal parents of an outcome variable. LD3 performs a linear number of conditional independence tests relative to variable set size, and allows for latent confounding under the sufficient condition that no parent of the outcome is latent. We show that LD3 returns a valid adjustment set (VAS) under a new graphical criterion for the weighted controlled direct effect, a qualitative indicator of direct discrimination. LD3 limits unnecessary adjustment, providing interpretable VAS for assessing unfairness. We use LD3 to analyze causal fairness in two complex decision systems: criminal recidivism prediction and liver transplant allocation. LD3 was more time-efficient and returned more plausible results on real-world data than baselines, which took 46x to 5870x longer to execute.<|reference_end|>
@article{maasch2024local,
    title={Local Causal Discovery for Structural Evidence of Direct Discrimination},
    author={Jacqueline Maasch, Kyra Gan, Violet Chen, Agni Orfanoudaki, Nil-Jana
  Akpinar, Fei Wang},
    journal={arXiv preprint arXiv:2405.14848},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14848},
    primaryClass={stat.ML cs.LG}
}