  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 (ML) 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 attributes have no (direct or indirect) causal effect on the target.
We propose rank-preserving interventional distributions to define a specific
FiND world in which this holds and a warping method for estimation. Evaluation
criteria for both the method and the resulting ML model are presented and
validated through simulations. Experiments on empirical data showcase the
practical application of our method and compare results with "fairadapt"
(Ple\v{c}ko and Meinshausen, 2020), a different approach for mitigating
unfairness by causally preprocessing data that uses quantile regression
forests. With this, we show that our warping approach effectively identifies
the most discriminated individuals and mitigates unfairness.
