  Using the concept of principal stratification from the causal inference
literature, we introduce a new notion of fairness, called principal fairness,
for human and algorithmic decision-making. The key idea is that one should not
discriminate among individuals who would be similarly affected by the decision.
Unlike the existing statistical definitions of fairness, principal fairness
explicitly accounts for the fact that individuals can be impacted by the
decision. Furthermore, we explain how principal fairness differs from the
existing causality-based fairness criteria. In contrast to the counterfactual
fairness criteria, for example, principal fairness considers the effects of
decision in question rather than those of protected attributes of interest. We
briefly discuss how to approach empirical evaluation and policy learning
problems under the proposed principal fairness criterion.
