  Individualized treatment rules, cornerstones of precision medicine, inform
patient treatment decisions with the goal of optimizing patient outcomes. These
rules are generally unknown functions of patients' pre-treatment covariates,
meaning they must be estimated from clinical or observational study data.
Myriad methods have been developed to learn these rules, and these procedures
are demonstrably successful in traditional asymptotic settings with moderate
number of covariates. The finite-sample performance of these methods in
high-dimensional covariate settings, which are increasingly the norm in modern
clinical trials, has not been well characterized, however. We perform a
comprehensive comparison of state-of-the-art individualized treatment rule
estimators, assessing performance on the basis of the estimators' accuracy,
interpretability, and computational efficacy. Sixteen data-generating processes
with continuous outcomes and binary treatment assignments are considered,
reflecting a diversity of randomized and observational studies. We summarize
our findings and provide succinct advice to practitioners needing to estimate
individualized treatment rules in high dimensions. All code is made publicly
available, facilitating modifications and extensions to our simulation study. A
novel pre-treatment covariate filtering procedure is also proposed and is shown
to improve estimators' accuracy and interpretability.
