Exposure to mixtures of chemicals, such as drugs, pollutants, and nutrients,
is common in real-world exposure or treatment scenarios. To understand the
impact of these exposures on health outcomes, an interpretable and important
approach is to estimate the causal effect of exposure regions that are most
associated with a health outcome. This requires a statistical estimator that
can identify these exposure regions and provide an unbiased estimate of a
causal target parameter given the region. In this work, we present a
methodology that uses decision trees to data-adaptively determine exposure
regions and employs cross-validated targeted maximum likelihood estimation to
unbiasedly estimate the average regional-exposure effect (ARE). This results in
a plug-in estimator with an asymptotically normal distribution and minimum
variance, from which confidence intervals can be derived. The methodology is
implemented in the open-source software, CVtreeMLE, a package in R. Analysts
put in a vector of exposures, covariates and an outcome and tables are given
for regions in the exposures, such as lead > 2.1 & arsenic > 1.4, with an
associated ARE which represents the mean outcome difference if all individuals
were exposed to this region compared to if none were exposed to this region.
CVtreeMLE enables researchers to discover interpretable exposure regions in
mixed exposure scenarios and provides robust statistical inference for the
impact of these regions. The resulting quantities offer interpretable
thresholds that can inform public health policies, such as pollutant
regulations, or aid in medical decision-making, such as identifying the most
effective drug combinations