Uplift modeling is an instrument used to estimate the change in outcome due to a treatment at the individual entity level. Uplift models assist decision-makers in opti- mally 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 con- cerning 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 benchmark- ing 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 treat- ment 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 con- cluded that performance largely depends on the context and problem characteristics. The newly proposed techniques are found to offer similar performances compared to state-of-the-art approaches.
Original languageEnglish
Number of pages36
JournalData Mining and Knowledge Discovery
Volume2020
DOIs
Publication statusPublished - 13 Jan 2020

    Research areas

  • Uplift modeling, Business analytics, Prescriptive analytics, Causality, Matching, Literature survey

ID: 48965298