A decision support framework to implement optimal personalized marketing interventions

Abstract

In many important settings, subjects can show significant heterogeneity in response to a stimulus or “treatment.” For instance, a treatment that works for the overall population might be highly ineffective, or even harmful, for a subgroup of subjects with specific characteristics. Similarly, a new treatment may not be better than an existing treatment in the overall population, but there is likely a subgroup of subjects who would benefit from it. The notion that “one size may not fit all” is becoming increasingly recognized in a wide variety of fields, ranging from economics to medicine. This has drawn significant attention to personalize the choice of treatment, so it is optimal for each individual. An optimal personalized treatment is the one that maximizes the probability of a desirable outcome. We call the task of learning the optimal personalized treatment personalized treatment learning. From the statistical learning perspective, this problem imposes important challenges, primarily because the optimal treatment is unknown on a given training set. A number of statistical methods have been proposed recently to tackle this problem. However, considering the critical importance of these methods to decision support systems, personalized treatment learning models have received relatively little attention in the literature. The purpose of this paper is to propose a novel method labeled causal conditional inference trees and its natural extension to causal conditional inference forests. The performance of the new method is analyzed and compared to alternative methods for personalized treatment learning. The results show that our new proposed method often outperforms the alternatives on the numerical settings described in this article. We also illustrate an application of the proposed method using data from a large Canadian insurer for the purpose of selecting the best targets for cross-selling an insurance product.