Prediction-powered Generalization of Causal Inferences
This addresses the challenge of applying trial results to broader populations for researchers and practitioners in causal inference, offering a novel approach but with incremental improvements over prior work.
The paper tackles the problem of generalizing causal inferences from randomized controlled trials (RCTs) to target populations with different effect modifier distributions, showing that traditional methods are statistically infeasible due to trial size limitations. It develops algorithms that supplement trial data with prediction models from observational studies, achieving better generalization with high-quality data and robustness to issues like unmeasured confounding.
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 an additional observational study (OS), without making any assumptions 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, and e.g., have unmeasured confounding.