Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts
Fixing proxy measurements when conditions shift between experiments
When researchers use quick proxy measurements instead of slower primary ones, distribution shifts between experiments can introduce hidden bias. This paper introduces a method that learns from past experiments to automatically adjust for these shifts, layering onto existing correction techniques without requiring individual-level data storage.
Many fields rely on proxy measurements for speed—clinical trials using biomarkers instead of patient outcomes, industrial testing using sensor readings instead of final quality checks. Current methods fail when conditions drift between experiments. This adjustment works on top of existing corrections and requires only summary-level historical data, making it practical to implement across domains while reducing the risk of biased conclusions.