  Uplift modeling is an emerging machine learning approach for estimating the
treatment effect at an individual or subgroup level. It can be used for
optimizing the performance of interventions such as marketing campaigns and
product designs. Uplift modeling can be used to estimate which users are likely
to benefit from a treatment and then prioritize delivering or promoting the
preferred experience to those users. An important but so far neglected use case
for uplift modeling is an experiment with multiple treatment groups that have
different costs, such as for example when different communication channels and
promotion types are tested simultaneously. In this paper, we extend standard
uplift models to support multiple treatment groups with different costs. We
evaluate the performance of the proposed models using both synthetic and real
data. We also describe a production implementation of the approach.
