  Counterfactual fairness is an approach to AI fairness that tries to make
decisions based on the outcomes that an individual with some kind of sensitive
status would have had without this status. This paper proposes Double Machine
Learning (DML) Fairness which analogises this problem of counterfactual
fairness in regression problems to that of estimating counterfactual outcomes
in causal inference under the Potential Outcomes framework. It uses arbitrary
machine learning methods to partial out the effect of sensitive variables on
nonsensitive variables and outcomes. Assuming that the effects of the two sets
of variables are additively separable, outcomes will be approximately equalised
and individual-level outcomes will be counterfactually fair. This paper
demonstrates the approach in a simulation study pertaining to discrimination in
workplace hiring and an application on real data estimating the GPAs of law
school students. It then discusses when it is appropriate to apply such a
method to problems of real-world discrimination where constructs are
conceptually complex and finally, whether DML Fairness can achieve justice in
these settings.
