Data-Driven Uplift Modeling

Uplift modeling studies the impact of different treatments on an outcome and has several applications, such as targeted marketing and medical therapy assessment. Usually, such studies are retrospective in nature rather than controlled experiments; hence, pre-existing bias in the observed data can distort the true effect of treatment. Previous studies on uplift modeling has largely overlooked this bias, focusing instead on model-based solutions for specific applications. In this paper, we introduce a new data-driven framework for uplift modeling based on matching. We represent individuals in treatment and control groups using weighted bipartite graphs, and bipartite graph matching is then employed to retain similar individuals, thereby reducing bias. Subsequently, appropriate datasets are generated from the matched pairs for learning predictive models for individuals. Our proposed model-independent framework facilitates robust inferences, making it adaptable to a wide range of applications and settings. In particular, we demonstrate how uplift modeling can be applied to discrimination discovery and prevention in discrimination-aware data mining. We evaluate our framework on three datasets representing three different applications. The results reveal that our framework produces better prediction performances in comparison to others and is easy to apply in practice.