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Estimating Heterogeneous Treatment Effect on Multivariate Responses Using Random Forests

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Abstract

Estimating the individualized treatment effect has become one of the most popular topics in statistics and machine learning communities in recent years. Most existing methods focus on modeling the heterogeneous treatment effects for univariate outcomes. However, many biomedical studies are interested in studying multiple highly correlated endpoints at the same time. We propose a random forest model that simultaneously estimates individualized treatment effects of multivariate outcomes. We consider a popular study design where covariates and outcomes are measured both before and after the intervention. The proposed model uses oblique splitting rules to partition population space to the neighborhood that experiences distinct treatment effects. An extensive simulation study suggests that the proposed method outperforms existing methods in various nonlinear settings. We further apply the proposed method to two nutrition studies investigating the effects of food consumption on gastrointestinal microbiota composition and clinical biomarkers. The method has been implemented in a freely available R package MOTE.RF at https://github.com/boyiguo1/MOTE.RF.

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Code Availability

The method has been implemented in a freely available R package MOTE.RF at https://github.com/boyiguo1/MOTE.RF.

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Author information

Authors and Affiliations

  1. Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, 35244, USA

    Boyi Guo

  2. Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA

    Hannah D. Holscher

  3. National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA

    Loretta S. Auvil, Michael E. Welge & Colleen B. Bushell

  4. USDA, ARS, Beltsville Human Nutrition Research Center, Beltsville, MD, 20705, USA

    Janet A. Novotny & David J. Baer

  5. Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA

    Nicholas A. Burd & Naiman A. Khan

  6. Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, 61801, USA

    Ruoqing Zhu

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  1. Boyi Guo
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Correspondence to Ruoqing Zhu.

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Guo, B., Holscher, H.D., Auvil, L.S. et al. Estimating Heterogeneous Treatment Effect on Multivariate Responses Using Random Forests. Stat Biosci 15, 545–561 (2023). https://doi.org/10.1007/s12561-021-09310-w

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  • DOI: https://doi.org/10.1007/s12561-021-09310-w

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