  Fairness of classification and regression has received much attention
recently and various, partially non-compatible, criteria have been proposed.
The fairness criteria can be enforced for a given classifier or, alternatively,
the data can be adapated to ensure that every classifier trained on the data
will adhere to desired fairness criteria. We present a practical data adaption
method based on quantile preservation in causal structural equation models. The
data adaptation is based on a presumed counterfactual model for the data. While
the counterfactual model itself cannot be verified experimentally, we show that
certain population notions of fairness are still guaranteed even if the
counterfactual model is misspecified. The precise nature of the fulfilled
non-causal fairness notion (such as demographic parity, separation or
sufficiency) depends on the structure of the underlying causal model and the
choice of resolving variables. We describe an implementation of the proposed
data adaptation procedure based on Random Forests and demonstrate its practical
use on simulated and real-world data.
