Uplift Modeling for Multiple Treatments with Cost Optimization

Abstract

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

“…The aim is to target individuals on who the treatment will have the largest positive effect according to the predictions of the model. An analogous approach to uplift modeling is the estimation of heterogeneous treatment effects (Zhao and Harinen 2019). A large portion of this literature employs machine learning methods to estimate the conditional average treatment effect (CATE).…”

“…The former consists of k 2 simultaneous pairwise comparisons and seeks to identify the best rank order for each individual. The latter compares each treatment alternative against a control group and aims to determine the optimal action for each individual (Zhao and Harinen 2019). To maintain similarity with the current MTUM literature, this study applies to scenarios with multiple treatment groups, including a control group.…”

“…Last, the cost difference of applying treatments in MTUM is incorporated by Zhao and Harinen (2019). The authors adapt the X-Learner (Künzel et al 2019) and the R-Learner (Nie and Wager 2017) meta-learners to the multitreatment uplift setting and propose a net-value optimization framework to consider the cost of each treatment.…”

“…Although the evaluation of MTUM following this approach poses some challenges, examples of its implementation are found in Sawant et al (2018) and Zhao and Harinen (2019). The main difficulty lies in the fact that individuals in the test set are exposed to treatments at random and their predicted optimal treatment does not necessarily match the treatment that is observed.…”

“…Among the techniques are well-established standard algorithms, such as the Logistic regression and the Random forest. Moreover, four modified algorithms that estimate the uplift directly in multitreatment applications are considered: Causal K-nearest neighbor (Guelman 2015), CTS random forest (Zhao et al 2017b), ED random forest (Rzepakowski and Jaroszewicz 2012), X-Learner random forest and R-Learner random forest (Zhao and Harinen 2019). For the NUA and the MMOA, we use the binary Uplift random forest developed by (Guelman 2014) and the Multinomial logistic regression, respectively.…”