  Fair feature selection for classification decision tasks has recently
garnered significant attention from researchers. However, existing fair feature
selection algorithms fall short of providing a full explanation of the causal
relationship between features and sensitive attributes, potentially impacting
the accuracy of fair feature identification. To address this issue, we propose
a Fair Causal Feature Selection algorithm, called FairCFS. Specifically,
FairCFS constructs a localized causal graph that identifies the Markov blankets
of class and sensitive variables, to block the transmission of sensitive
information for selecting fair causal features. Extensive experiments on seven
public real-world datasets validate that FairCFS has comparable accuracy
compared to eight state-of-the-art feature selection algorithms, while
presenting more superior fairness.
