Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect - Artificial Intelligence Applications and Innovations Access content directly
Conference Papers Year : 2022

Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect

Zhenyu Zhao
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Yumin Zhang
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Totte Harinen
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Mike Yung
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Abstract

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, this paper introduces a set of feature selection methods explicitly designed for uplift modeling, drawing inspiration from statistics and information theory. Empirical evaluations are conducted 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.
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hal-04668649 , version 1 (07-08-2024)

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Zhenyu Zhao, Yumin Zhang, Totte Harinen, Mike Yung. Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.217-230, ⟨10.1007/978-3-031-08337-2_19⟩. ⟨hal-04668649⟩
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