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

TL;DR

This paper presents InterXshift, a novel nonparametric method using stochastic interventions and machine learning to discover and estimate interactions among multiple exposures in high-dimensional data, with applications in environmental health.

Contribution

It introduces a model-independent approach for interaction discovery and estimation, advancing analysis of complex exposure effects with open-source software implementation.

Findings

Successfully identified true interaction directions in simulations.

Demonstrated significant impact of furan exposure on telomere length.

Validated method's consistency and efficacy in real data application.

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.