Uplift Modeling for Multiple Treatments with Cost Optimization 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.

A survey and benchmarking study of multitreatment uplift modeling Uplift modeling is an instrument used to estimate the change in outcome due to a treatment at the individual entity level. Uplift models assist decisionmakers in optimally allocating scarce resources. This allows the selection of the subset of entities for which the effect of a treatment will be largest and, as such, the maximization of the overall returns. The literature on uplift modeling mostly focuses on queries concerning the effect of a single treatment and rarely considers situations where more than one treatment alternative is utilized. This article surveys the current literature on multitreatment uplift modeling and proposes two novel techniques: the naive uplift approach and the multitreatment modified outcome approach. Moreover, a benchmarking experiment is performed to contrast the performances of different multitreatment uplift modeling techniques across eight data sets from various domains. We verify and, if needed, correct the imbalance among the pretreatment characteristics of the treatment groups by means of optimal propensity score matching, which ensures a correct interpretation of the estimated uplift. Conventional and recently proposed evaluation metrics are adapted to the multitreatment scenario to assess performance. None of the evaluated techniques consistently outperforms other techniques. Hence, it is concluded that performance largely depends on the context and problem characteristics. The newly proposed techniques are found to offer similar performances compared to stateoftheart approaches.