Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect
This work addresses a practical issue in causal learning for applications like targeted advertising, offering incremental improvements in feature selection for uplift models.
The paper tackles the problem of feature selection in uplift modeling, where traditional methods are unsuitable due to differences in target objectives, by introducing new methods designed specifically for this task, showing advantages over traditional approaches in empirical evaluations on public datasets.
Uplift modeling is a causal learning technique that estimates subgroup-level treatment effects. It is commonly used in industry and elsewhere for tasks such as targeting ads. In a typical setting, uplift models can take thousands of features as inputs, which is costly and results in problems such as overfitting and poor model interpretability. Consequently, there is a need to select a subset of the most important features for modeling. However, traditional methods for doing feature selection are not fit for the task because they are designed for standard machine learning models whose target is importantly different from uplift models. To address this, we introduce a set of feature selection methods explicitly designed for uplift modeling, drawing inspiration from statistics and information theory. We conduct empirical evaluations on the proposed methods on publicly available datasets, demonstrating the advantages of the proposed methods compared to traditional feature selection. We make the proposed methods publicly available as a part of the CausalML open-source package.