Prediction-powered Generalization of Causal Inferences

Causal inferences from a randomized controlled trial (RCT) may not pertain to a target population where some effect modifiers have a different distribution. Prior work studies generalizing the 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 additional observational data (OD), without making any assumptions on the OD. We theoretically and empirically show that our methods facilitate better generalization when the OD is high-quality, and remain robust when it is not, and e.g., have unmeasured confounding.