Assumption-Lean and Data-Adaptive Post-Prediction Inference

TL;DR

This paper introduces PSPA, a new inference method that provides valid, powerful statistical analysis using ML-predicted data without relying on assumptions about the prediction, improving efficiency and reliability.

Contribution

The paper presents PSPA, an assumption-lean, data-adaptive inference method that ensures valid statistical conclusions from ML-predicted outcomes, regardless of prediction accuracy.

Findings

PSPA guarantees valid inference without assumptions on ML predictions.

PSPA demonstrates efficiency gains over existing methods.

Validated through simulations and real-data applications.

Abstract

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.