Interpretable Multiple Treatment Revenue Uplift Modeling
This work addresses the problem of optimizing treatment selection and estimating continuous revenue uplift for businesses, offering an incremental improvement over existing single-treatment, binary-outcome models.
This paper extends uplift modeling to handle multiple treatments and continuous outcomes, enabling the selection of an optimal treatment and estimation of treatment effects as continuous business outcomes. The authors develop revenue uplift models using a causal forest algorithm, demonstrating advantages over benchmarks on two real-world marketing datasets.
Big data and business analytics are critical drivers of business and societal transformations. Uplift models support a firm's decision-making by predicting the change of a customer's behavior due to a treatment. Prior work examines models for single treatments and binary customer responses. The paper extends corresponding approaches by developing uplift models for multiple treatments and continuous outcomes. This facilitates selecting an optimal treatment from a set of alternatives and estimating treatment effects in the form of business outcomes of continuous scale. Another contribution emerges from an evaluation of an uplift model's interpretability, whereas prior studies focus almost exclusively on predictive performance. To achieve these goals, the paper develops revenue uplift models for multiple treatments based on a recently introduced algorithm for causal machine learning, the causal forest. Empirical experimentation using two real-world marketing data sets demonstrates the advantages of the proposed modeling approach over benchmarks and standard marketing practices.