To Treat, or Not to Treat: Reducing Volatility in Uplift Modeling Through Weighted Ensembles

Abstract

When conducting direct marketing activities, companies strive to know whom to target with a marketing incentive to maximize the campaign effect. For example, which customer should receive churn prevention incentive to minimize overall churn rate? Uplift modeling is a promising approach to answer such a question. It allows to separate customers who would likely react positively to a treatment from those who would remain neutral or even react negatively. However, while different uplift approaches have been proposed, they usually suffer from high volatility and their performance often depends largely on data set and application context. Thus, it is difficult for practitioners and researchers to apply uplift modeling. To overcome these problems, we propose a weighted ensemble of different uplift modeling approaches to reduce volatility and improve robustness. We evaluate the novel approach against single uplift modeling approaches on multiple data sets and find that the ensemble is indeed more robust.

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