  Individuals can vary drastically in their response to the same treatment, and
this heterogeneity has driven the push for more personalized medicine. Accurate
and interpretable methods to identify subgroups that respond to the treatment
differently from the population average are necessary to achieving this goal.
The Virtual Twins (VT) method by Foster et al. \cite{Foster} is a highly cited
and implemented method for subgroup identification because of its intuitive
framework. However, since its initial publication, many researchers still rely
heavily on the authors' initial modeling suggestions without examining newer
and more powerful alternatives. This leaves much of the potential of the method
untapped. We comprehensively evaluate the performance of VT with different
combinations of methods in each of its component steps, under a collection of
linear and nonlinear problem settings. Our simulations show that the method
choice for step 1 of VT is highly influential in the overall accuracy of the
method, and Superlearner is a promising choice. We illustrate our findings by
using VT to identify subgroups with heterogeneous treatment effects in a
randomized, double-blind nicotine reduction trial.
