  When assessing causal effects, determining the target population to which the
results are intended to generalize is a critical decision. Randomized and
observational studies each have strengths and limitations for estimating causal
effects in a target population. Estimates from randomized data may have
internal validity but are often not representative of the target population.
Observational data may better reflect the target population, and hence be more
likely to have external validity, but are subject to potential bias due to
unmeasured confounding. While much of the causal inference literature has
focused on addressing internal validity bias, both internal and external
validity are necessary for unbiased estimates in a target population. This
paper presents a framework for addressing external validity bias, including a
synthesis of approaches for generalizability and transportability, the
assumptions they require, as well as tests for the heterogeneity of treatment
effects and differences between study and target populations.
