Prediction-Powered Inference

I will discuss prediction-powered inference – a strategy for creating confidence intervals using machine-learning predictions and a small amount of ground-truth labels. When the predictions are accurate, the intervals shrink, yielding better statistical power. The validity of the intervals holds regardless of the machine-learning algorithm. Intervals can be computed for many estimands, such as means, quantiles, and linear and logistic regression coefficients. We demonstrate the benefits of prediction-powered inference with data sets from proteomics, genomics, electronic voting, remote sensing, census analysis, and ecology. The talk will draw from the following jointly authored papers: [1], [2], [3].