Fairness testing for uplift models

Uplift modeling was first initiated in the industry in the early 2000s as a new methodology to improve marketing efficiency by predicting individual treatment effect (ITE). It estimates the conditional average treatment effect (CATE) as the difference in outcome probabilities with and without treatment. Recently, AI ethics including fairness evaluation has received significant attention from academia, industry, and regulatory agencies. However, standard fairness metrics, originally developed for conventional predictive models, generally require ground truth (ITE) and cannot be applied directly for uplift models. In this paper, we propose a novel and practical approach to compute fairness metrics suitable for uplift models. A formal framework is first established based on probability theory. It is followed by a simulation analysis to demonstrate its effectiveness. Finally, we illustrate how to apply the approach through an example using public data.