  Recent research in causal inference has made important progress in addressing
challenges to the external validity of trial findings. Such methods weight
trial participant data to more closely resemble the distribution of
effect-modifying covariates in a well-defined target population. In the
presence of participant non-adherence to study medication, these methods
effectively transport an intention-to-treat effect that averages over
heterogeneous compliance behaviors. In this paper, we develop a principal
stratification framework to identify causal effects conditioning on both
compliance behavior and membership in the target population. We also develop
non-parametric efficiency theory for and construct efficient estimators of such
"transported" principal causal effects and characterize their finite-sample
performance in simulation experiments. While this work focuses on treatment
non-adherence, the framework is applicable to a broad class of estimands that
target effects in clinically-relevant, possibly latent subsets of a target
population.
