Prediction-powered Generalization of Causal Inferences

Causal inferences from a randomized controlled trial (RCT) may not pertain to atargetpopulation where some effect modifiers have a different distribution. Prior work studiesgeneralizingthe results of a trial to a target population with no outcome but covariate data available. We show how the limited size of trials makes generalization a statistically infeasible task, as it requires estimating complex nuisance functions. We develop generalization algorithms that supplement the trial data with a prediction model learned from an additionalobservationalstudy (OS), without makinganyassumptions on the OS. We theoretically and empirically show that our methods facilitate better generalization when the OS is "high-quality", and remain robust when it is not, ande.g., have unmeasured confounding.