Uplift Modelling via Gradient Boosting

Abstract

The Gradient Boosting machine learning ensemble algorithm, well-known for its proficiency and superior performance in intricate machine learning tasks, has encountered limited success in the realm of uplift modeling. Uplift modeling is a challenging task that necessitates a known target for the precise computation of the training gradient. The prevailing two-model strategies, which separately model treatment and control outcomes, are encumbered with limitations as they fail to directly tackle the uplift problem.
This paper presents an innovative approach to uplift modeling that employs Gradient Boosting. Unlike previous works, our algorithm utilizes multioutput boosting model and calculates the uplift gradient based on intermediate surrogate predictions and directly models the concealed target. This method circumvents the requirement for a known target and addresses the uplift problem more effectively than existing solutions.
Moreover, we broaden the scope of this solution to encompass multitreatment settings, thereby enhancing its applicability. This novel approach not only overcomes the limitations of the traditional two-model strategies but also paves the way for more effective and efficient uplift modeling using Gradient Boosting.