We survey the many roles that causal reasoning plays in reasoning about fairness in machine learning. While the existing scholarship on causal approaches to fairness in machine learning has focused on the degree to which features in a model might have been causally affected by (discrimination on the basis of) sensitive features, causal reasoning also plays an important---if more implicit---role in other ways of assessing the fairness of models. This paper therefore tries to distinguish and disentangle the many roles that causal reasoning plays in reasoning about fairness, with the additional goal of asking how causality is thought to help achieve these normative goals and to what extent this is possible or necessary.