Prediction-powered Generalization of Causal Inferences
Ilker Demirel, Ahmed Alaa, Anthony Philippakis, David Sontag
Abstract
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.
BibTeX
@inproceedings{
demirel2024predictionpowered,
title={Prediction-powered Generalization of Causal Inferences},
author={Ilker Demirel and Ahmed Alaa and Anthony Philippakis and David Sontag},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=QKnWXX3aVm}
}