proxymate: Diagnosis and Adjustment of Proxy Estimates for Reliable Inference
2026-07-27 • Machine Learning
Machine Learning
AI summaryⓘ
The authors introduce proxymate, a tool that helps check and fix problems when using proxy outcomes instead of the main results, which can be slow or hard to measure. Their framework looks at different levels, like whether the data represents the population well or if measurements are reliable, and offers ways to adjust for issues found. They tested and used proxymate at Meta in various situations, such as experiments and monitoring, improving decision-making by correcting many proxy-related errors. This shows how their approach can make working with proxy data more accurate and dependable.
proxy outcomessurrogate endpointsconfidence intervalspopulation validitymeasurement qualitydecision validitycross-domain transportabilitydiagnostic checksdata adjustmentexperimentation
Authors
Alexandra N. M. Darmon, Deeksha Sinha, Steve Wilkins-Reeves, Caner Gocmen
Abstract
Proxy outcomes (such as short-term behavioral signals, model predictions, or surrogate endpoints) are frequently used in place of primary outcomes that are too slow to mature, rare, or challenging to measure directly. But valid inference on a proxy does not guarantee valid inference on the primary estimate as proxy-based estimates can be systematically biased in ways that are difficult to predict, leading to improperly calibrated confidence intervals. We present proxymate, a framework and open-source Python package for proxy validation and adjustment. proxymate organizes into four levels: The Representativity Level (population validity), the Unit Level (measurement quality), the Estimate Level (decision validity), and the Domain Level (cross-domain transportability). Within each level, proxymate provides diagnostic checks, and targeted adjustment strategies that map specific failures to appropriate corrections. At Meta, proxymate has been adopted by many different use cases, spanning experimentation, prevalence estimation, and monitoring use cases, all facing different proxy challenges (limited human review time, long maturation window of outcomes, low detectability) and showcasing the modularity of the framework. Across all products, proxymate assessed and corrected millions of proxy, primary unit comparisons. It has facilitated launches across multiple work streams including enabling quick decision making on thousands of experiments.