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Semiparametric Discovery and Estimation of Interaction in Mixed Exposures using Stochastic Interventions

Authors :
McCoy, David B.
Hubbard, Alan E.
Schuler, Alejandro
van der Laan, Mark J.
Publication Year :
2023

Abstract

This study introduces a nonparametric definition of interaction and provides an approach to both interaction discovery and efficient estimation of this parameter. Using stochastic shift interventions and ensemble machine learning, our approach identifies and quantifies interaction effects through a model-independent target parameter, estimated via targeted maximum likelihood and cross-validation. This method contrasts the expected outcomes of joint interventions with those of individual interventions. Validation through simulation and application to the National Institute of Environmental Health Sciences Mixtures Workshop data demonstrate the efficacy of our method in detecting true interaction directions and its consistency in identifying significant impacts of furan exposure on leukocyte telomere length. Our method, called InterXshift, advances the ability to analyze multi-exposure interactions within high-dimensional data, offering significant methodological improvements to understand complex exposure dynamics in health research. We provide peer-reviewed open-source software that employs or proposed methodology in the InterXshift R package.

Subjects

Subjects :
Statistics - Methodology

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2305.01849
Document Type :
Working Paper