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arXiv · 2307.14766

Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of main effects

Mayu Hiraishi·Ke Wan·Kensuke Tanioka·Hiroshi Yadohisa·Toshio Shimokawa
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Abstract

This study proposes a novel framework based on the RuleFit method to estimate Heterogeneous Treatment Effect (HTE) in a randomized clinical trial. To achieve this, we adopted S-learner of the metaalgorithm for our proposed framework. The proposed method incorporates a rule term for the main effect and treatment effect, which allows HTE to be interpretable form of rule. By including a main effect term in the proposed model, the selected rule is represented as an HTE that excludes other effects. We confirmed a performance equivalent to that of another ensemble learning methods through numerical simulation and demonstrated the interpretation of the proposed method from a real data application.

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Mayu Hiraishi, Ke Wan, Kensuke Tanioka, Hiroshi Yadohisa, Toshio Shimokawa. 2023-07-27. Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of main effects. https://arxiv.org/abs/2307.14766

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