  Fair machine learning aims to prevent discrimination against individuals or
sub-populations based on sensitive attributes such as gender and race. In
recent years, causal inference methods have been increasingly used in fair
machine learning to measure unfairness by causal effects. However, current
methods assume that the true causal graph is given, which is often not true in
real-world applications. To address this limitation, this paper proposes a
framework for achieving causal fairness based on the notion of interventions
when the true causal graph is partially known. The proposed approach involves
modeling fair prediction using a Partially Directed Acyclic Graph (PDAG),
specifically, a class of causal DAGs that can be learned from observational
data combined with domain knowledge. The PDAG is used to measure causal
fairness, and a constrained optimization problem is formulated to balance
between fairness and accuracy. Results on both simulated and real-world
datasets demonstrate the effectiveness of this method.
