Achieving counterfactual fairness with imperfect structural causal model

Counterfactual fairness alleviates the discrimination between the model prediction toward an individual in the actual world (observational data) and that in counterfactual world (i.e., what if the individual belongs to other sensitive groups). The existing studies need to pre-define the structural causal model that captures the correlations among variables for counterfactual inference; however, the underlying causal model is usually unknown and difficult to be validated in real-world scenarios. Moreover, the misspecification of the causal model potentially leads to poor performance in model prediction and thus makes unfair decisions. In this research, we propose a novel minimax game-theoretic model for counterfactual fairness that can produce accurate results meanwhile achieve a counterfactually fair decision with the relaxation of strong assumptions of structural causal models. In addition, we also theoretically prove the error bound of the proposed minimax model. Empirical experiments on multiple real-world datasets illustrate our superior performance in both accuracy and fairness. For further reference, the source code associated with this research is available.1

Automated summary

Counterfactual fairness addresses discrimination between model predictions in the actual and counterfactual worlds. This research proposes a minimax game-theoretic model that achieves counterfactual fairness without strict assumptions on structural causal models.