  The importance of algorithmic fairness grows with the increasing impact
machine learning has on people's lives. Recent work on fairness metrics shows
the need for causal reasoning in fairness constraints. In this work, a
practical method named FairTrade is proposed for creating flexible prediction
models which integrate fairness constraints on sensitive causal paths. The
method uses recent advances in variational inference in order to account for
unobserved confounders. Further, a method outline is proposed which uses the
causal mechanism estimates to audit black box models. Experiments are conducted
on simulated data and on a real dataset in the context of detecting unlawful
social welfare. This research aims to contribute to machine learning techniques
which honour our ethical and legal boundaries.
