SPARC: Scalable Path-Specific Counterfactual Fairness via Causal Conditional Independence

Deep learning models exhibit fairness concerns when pre-dictions are inadvertently influenced by sensitive attributes. However,existing attempts to make Path-Specific Counterfactual Fairness opti-mizable rely on estimating marginal potential outcome probabilities—anapproach that fundamentally requires high-dimensional conditional den-sity estimation and breaks down in modalities such as medical images,where the curse of dimensionality renders reliable estimation infeasible.To address this limitation, we reduce the problem of enforcing Path-Specific Counterfactual Fairness to a causal conditional independenceconstraint and prove that satisfying this constraint is sufficient to elim-inate the unfair causal effect. This reduction replaces intractable coun-terfactual estimation with a discriminative optimization objective thatremains scalable in high-dimensional settings.