  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.
