  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.
