  Fairness is increasingly recognized as a critical component of machine
learning systems. However, it is the underlying data on which these systems are
trained that often reflects discrimination, suggesting a data management
problem. In this paper, we first make a distinction between associational and
causal definitions of fairness in the literature and argue that the concept of
fairness requires causal reasoning. We then review existing works and identify
future opportunities for applying data management techniques to causal
algorithmic fairness.
