Estimating Heterogeneous Treatment Effect on Multivariate Responses Using Random Forests

Boyi Guo, Hannah D. Holscher, Loretta S. Auvil, Michael E. Welge, Colleen B. Bushell, Janet A. Novotny, David J. Baer, Nicholas A. Burd, Naiman A. Khan and Ruoqing Zhu ()
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Boyi Guo: University of Alabama at Birmingham
Hannah D. Holscher: University of Illinois at Urbana-Champaign
Loretta S. Auvil: University of Illinois at Urbana-Champaign
Michael E. Welge: University of Illinois at Urbana-Champaign
Colleen B. Bushell: University of Illinois at Urbana-Champaign
Janet A. Novotny: USDA, ARS, Beltsville Human Nutrition Research Center
David J. Baer: USDA, ARS, Beltsville Human Nutrition Research Center
Nicholas A. Burd: University of Illinois at Urbana-Champaign
Naiman A. Khan: University of Illinois at Urbana-Champaign
Ruoqing Zhu: University of Illinois at Urbana-Champaign

Statistics in Biosciences, 2023, vol. 15, issue 3, No 2, 545-561

Abstract: 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 .

Keywords: Individualized treatment effect; Microbiota; Multivariate; Random forests; Personalized nutrition (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12561-021-09310-w

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