A generalizability score for aggregate causal effect

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.

Automated summary

Generalizing population level causal quantities from a source population to a target population can be difficult and unreliable when there are heterogenous causal effects and differences in subject characteristics. To address this issue, we propose a generalizability score that can be used as a yardstick to select target subpopulations and prevent biases associated with inadvertent access to outcome information.