  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.
