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A primary challenge facing modern scientific research is the limitedavailability of gold-standard data which can be both costly and labor-intensiveto obtain. With the rapid development of machine learning (ML), scientists haverelied on ML algorithms to predict these gold-standard outcomes with easilyobtained covariates. However, these predicted outcomes are often used directlyin subsequent statistical analyses, ignoring imprecision and heterogeneityintroduced by the prediction procedure. This will likely result in falsepositive findings and invalid scientific conclusions. In this work, weintroduce an assumption-lean and data-adaptive Post-Prediction Inference(POP-Inf) procedure that allows valid and powerful inference based onML-predicted outcomes. Its "assumption-lean" property guarantees reliablestatistical inference without assumptions on the ML-prediction, for a widerange of statistical quantities. Its "data-adaptive'" feature guarantees anefficiency gain over existing post-prediction inference methods, regardless ofthe accuracy of ML-prediction. We demonstrate the superiority and applicabilityof our method through simulations and large-scale genomic data.

Assumption-lean and Data-adaptive Post-Prediction Inference