  Prediction-powered inference is a framework for performing valid statistical
inference when an experimental dataset is supplemented with predictions from a
machine-learning system. The framework yields simple algorithms for computing
provably valid confidence intervals for quantities such as means, quantiles,
and linear and logistic regression coefficients, without making any assumptions
on the machine-learning algorithm that supplies the predictions. Furthermore,
more accurate predictions translate to smaller confidence intervals.
Prediction-powered inference could enable researchers to draw valid and more
data-efficient conclusions using machine learning. The benefits of
prediction-powered inference are demonstrated with datasets from proteomics,
astronomy, genomics, remote sensing, census analysis, and ecology.
