Uplift Random Forests

Conventional supervised statistical learning models aim to achieve high accuracy in predicting the value of an outcome measure based on a number of input measures. However, in many applications, some type of action is randomized on the observational units. This is the case, for example, in treatment/control settings, such as those usually encountered in marketing and clinical trial applications. In these situations, we may not necessarily be interested in predicting the outcome itself, but in estimating the expected change in the outcome as a result of the action. This is precisely the idea behind uplift models, which, despite their many practical applications, have received little attention in the literature. In this article, we extend the state-of-the-art research in this area by proposing a new approach based on Random Forests. We perform carefully designed experiments using simple simulation models to illustrate some of the properties of the proposed method. In addition, we present evidence on a dataset pertaining to a large Canadian insurer on a customer retention case. The results confirm the effectiveness of the proposed method and show favorable performance relative to other existing uplift modeling approaches.