  Transportability provides a principled framework to address the problem of
applying study results to new populations. Here, we consider the problem of
selecting variables to include in transport estimators. We provide a brief
overview of the transportability framework and illustrate that while selection
diagrams are a vital first step in variable selection, these graphs alone
identify a sufficient but not strictly necessary set of variables for
generating an unbiased transport estimate. Next, we conduct a simulation
experiment assessing the impact of including unnecessary variables on the
performance of the parametric g-computation transport estimator. Our results
highlight that the types of variables included can affect the bias, variance,
and mean squared error of the estimates. We find that addition of variables
that are not causes of the outcome but whose distributions differ between the
source and target populations can increase the variance and mean squared error
of the transported estimates. On the other hand, inclusion of variables that
are causes of the outcome (regardless of whether they modify the causal
contrast of interest or differ in distribution between the populations) reduces
the variance of the estimates without increasing the bias. Finally, exclusion
of variables that cause the outcome but do not modify the causal contrast of
interest does not increase bias. These findings suggest that variable selection
approaches for transport should prioritize identifying and including all causes
of the outcome in the study population rather than focusing on variables whose
distribution may differ between the study sample and target population.
