  Algorithmic fairness is typically studied from the perspective of
predictions. Instead, here we investigate fairness from the perspective of
recourse actions suggested to individuals to remedy an unfavourable
classification. We propose two new fairness criteria at the group and
individual level, which -- unlike prior work on equalising the average
group-wise distance from the decision boundary -- explicitly account for causal
relationships between features, thereby capturing downstream effects of
recourse actions performed in the physical world. We explore how our criteria
relate to others, such as counterfactual fairness, and show that fairness of
recourse is complementary to fairness of prediction. We study theoretically and
empirically how to enforce fair causal recourse by altering the classifier and
perform a case study on the Adult dataset. Finally, we discuss whether fairness
violations in the data generating process revealed by our criteria may be
better addressed by societal interventions as opposed to constraints on the
classifier.
