Assumption-Lean and Data-Adaptive Post-Prediction Inference
This addresses a critical issue for scientists using ML predictions in research, offering a robust solution to prevent false positives and improve efficiency, though it appears incremental as an enhancement to existing post-prediction methods.
The paper tackles the problem of invalid statistical inference when using machine learning-predicted outcomes in scientific analyses, introducing PSPA to provide valid and powerful inference without assumptions on the ML prediction and with guaranteed efficiency gains.
A primary challenge facing modern scientific research is the limited availability of gold-standard data which can be costly, labor-intensive, or invasive to obtain. With the rapid development of machine learning (ML), scientists can now employ ML algorithms to predict gold-standard outcomes with variables that are easier to obtain. However, these predicted outcomes are often used directly in subsequent statistical analyses, ignoring imprecision and heterogeneity introduced by the prediction procedure. This will likely result in false positive findings and invalid scientific conclusions. In this work, we introduce PoSt-Prediction Adaptive inference (PSPA) that allows valid and powerful inference based on ML-predicted data. Its "assumption-lean" property guarantees reliable statistical inference without assumptions on the ML prediction. Its "data-adaptive" feature guarantees an efficiency gain over existing methods, regardless of the accuracy of ML prediction. We demonstrate the statistical superiority and broad applicability of our method through simulations and real-data applications.