  Scientists frequently generalize population level causal quantities such as
average treatment effect from a source population to a target population. When
the causal effects are heterogeneous, differences in subject characteristics
between the source and target populations may make such a generalization
difficult and unreliable. Reweighting or regression can be used to adjust for
such differences when generalizing. However, these methods typically suffer
from large variance if there is limited covariate distribution overlap between
the two populations. We propose a generalizability score to address this issue.
The score can be used as a yardstick to select target subpopulations for
generalization. A simplified version of the score avoids using any outcome
information and thus can prevent deliberate biases associated with inadvertent
access to such information. Both simulation studies and real data analysis
demonstrate convincing results for such selection.
