Surrogate Ground Truth Generation to Enhance Binary Fairness Evaluation in Uplift Modeling

Uplift modeling is used extensively to identify treatment candidates that are more likely to benefit from an intervention. However, the fairness evaluation of such models remains a challenge due to the lack of ground truth on the outcome measure since a candidate cannot be in both treatment and control simultaneously. In this paper, we propose a framework that generates surrogate ground truth to serve as a proxy for counterfactual labels of uplm modeling campaigns. We then leverage the surrogate ground truth to conduct a more comprehensive binary fairness evaluation. We show how to apply the approach in a real-world marketing campaign for promotional offers and demonstrate its enhancement for fairness evaluation.