Uplift modeling with quasi-loss-functions

Abstract

Uplift modeling, also referred to as heterogeneous treatment effect estimation, is a machine learning technique utilized in marketing for estimating the incremental impact of treatment on the response of each customer. Uplift models face a fundamental challenge in causal inference because the variable of interest (i.e., the uplift itself) remains unobservable. As a result, popular uplift models (such as meta-learners and uplift trees) do not incorporate loss functions for uplifts in their algorithms. This article addresses that gap by proposing uplift models with quasi-loss functions (UpliftQL models), which separately use four specially designed quasi-loss functions for uplift estimation in algorithms. Using simulated data, our analysis reveals that, on average, 55% (34%) of the top five models from a set of 14 are UpliftQL models for binary (continuous) outcomes. Further empirical data analysis shows that over 60% of the top-performing models are consistently UpliftQL models.