Problem
Research questions and friction points this paper is trying to address.
Binary Outcome
Model Efficiency
Information Utilization
A New Transformation Approach for Uplift Modeling with Binary Outcome
🤖 AI Summary
To address the underutilization of zero-outcome samples and the neglect of deep structural information in treatment assignment within binary-outcome uplift modeling, this paper proposes a novel target-variable transformation method specifically designed for binary outcomes. The transformation preserves treatment assignment while explicitly encoding counterfactual differences in the target, enabling interpretable and efficient utilization of zero-outcome samples for the first time. Based on this transformation, we develop an end-to-end uplift learning framework that imposes no strong modeling assumptions, seamlessly integrating logistic regression with ensemble methods to ensure both theoretical soundness and engineering robustness. Extensive experiments on multiple synthetic and real-world datasets demonstrate significant performance gains over classical approaches—including S-Learner and X-Learner. The method has been deployed in a nationwide financial group’s precision marketing campaign, yielding a 12.7% improvement in response rate.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Binary Target Variables
Boosting Model Transformation
Predictive Accuracy Improvement
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