Causal Fair Machine Learning via Rank-Preserving Interventional
  Distributions
Ludwig Bothmann Susanne Dandl Michael Schomaker
A decision can be defined as fair if equal individuals are treated equally
and unequals unequally. Adopting this definition, the task of designing machine
learning models that mitigate unfairness in automated decision-making systems
must include causal thinking when introducing protected attributes. Following a
recent proposal, we define individuals as being normatively equal if they are
equal in a fictitious, normatively desired (FiND) world, where the protected
attribute has no (direct or indirect) causal effect on the target. We propose
rank-preserving interventional distributions to define an estimand of this FiND
world and a warping method for estimation. Evaluation criteria for both the
method and resulting model are presented and validated through simulations and
empirical data. With this, we show that our warping approach effectively
identifies the most discriminated individuals and mitigates unfairness.