  A primary concern of public health researchers involves identifying and
quantifying heterogeneous exposure effects across population subgroups.
Understanding the magnitude and direction of these effects on a given scale
provides researchers the ability to recommend policy prescriptions and assess
the external validity of findings. Furthermore, increasing popularity in fields
such as precision medicine that rely on accurate estimation of high-dimensional
interaction effects has highlighted the importance of understanding effect
modification. Traditional methods for effect measure modification analyses
include parametric regression modeling with either stratified analyses and
corresponding heterogeneity tests or including an interaction term in a
multivariable model. However, these methods require manual model specification
and are often impractical or not feasible to conduct by hand in
high-dimensional settings. Recent developments in machine learning aim to solve
this issue by automating heterogeneous subgroup identification and effect
estimation. In this paper, we summarize and provide the intuition behind modern
machine learning methods for effect measure modification analyses to serve as a
reference for public health researchers. We discuss their implementation in R,
provide annotated syntax and review available supplemental analysis tools by
assessing the heterogeneous effects of drought on stunting among children in
the Demographic and Health Survey data set as a case study.
