A New Transformation Approach for Uplift Modeling with Binary Outcome

TL;DR

This paper introduces a novel transformation method for uplift modeling with binary outcomes, enhancing efficiency by utilizing information from zero-outcome samples, and demonstrates superior performance over traditional methods.

Contribution

The paper proposes a new transformed outcome for binary uplift modeling that fully exploits zero-outcome samples, improving prediction accuracy and practical applicability.

Findings

Outperforms traditional transformation methods in experiments

Effective on both synthetic and real-world datasets

Applied successfully in a nationwide financial marketing context

Abstract

Uplift modeling has been used effectively in fields such as marketing and customer retention, to target those customers who are more likely to respond due to the campaign or treatment. Essentially, it is a machine learning technique that predicts the gain from performing some action with respect to not taking it. A popular class of uplift models is the transformation approach that redefines the target variable with the original treatment indicator. These transformation approaches only need to train and predict the difference in outcomes directly. The main drawback of these approaches is that in general it does not use the information in the treatment indicator beyond the construction of the transformed outcome and usually is not efficient. In this paper, we design a novel transformed outcome for the case of the binary target variable and unlock the full value of the samples with zero outcome. From a practical perspective, our new approach is flexible and easy to use. Experimental results on synthetic and real-world datasets obviously show that our new approach outperforms the traditional one. At present, our new approach has already been applied to precision marketing in a China nation-wide financial holdings group.