Idea
Framework diagnosing and correcting proxy outcome biases to ensure reliable decision-making across industries.
Research Paper
Core Innovation
This paper introduces proxymate, a modular framework that organizes proxy validation into four diagnostic levels addressing population validity, measurement quality, decision validity, and cross-domain transportability. It maps specific proxy failures to targeted adjustment strategies, enabling systematic correction of biases that prior methods often overlook.
Why It Matters
Many industries rely on proxy outcomes due to delays or difficulty in measuring primary outcomes directly, but proxy biases can lead to incorrect decisions. proxymate improves inference reliability by systematically diagnosing and adjusting these biases, enabling faster and more accurate decisions at scale. This reduces risk and accelerates workflows in experimentation and monitoring.
Market Size (TAM)
$2–10B TAM for data validation and decision support tools; $500M–$1B SAM from tech, healthcare, and market research sectors. Driven by increasing reliance on proxy data and demand for faster, reliable decision-making.
Potential Customers & Pain Points
- Tech companies – Need accurate experiment results despite delayed primary outcomes
- Healthcare researchers – Need reliable surrogate endpoint analysis
- Market analysts – Need valid prevalence estimates from proxy data
- Product teams – Need trustworthy monitoring metrics with limited human review.
Business Model
Open-source core with enterprise licensing for advanced features, custom integrations, and support services targeting large organizations with complex proxy inference needs.
Competitive Landscape
- Evidation Health
- Databricks
- Alteryx
- DataRobot
Implementation Challenges
- Integration complexity with existing data pipelines
- User trust in automated proxy adjustments
- Variability in proxy data quality across domains
Validation Strategy
- Pilot deployments in Meta’s experimentation and monitoring workflows
- Case studies demonstrating improved decision accuracy and speed
- Partnerships with healthcare and market research firms for domain validation
Research Paper Overview
proxymate: Diagnosis and Adjustment of Proxy Estimates for Reliable Inference
Summary
proxymate is a framework and Python package that diagnoses and adjusts proxy outcome estimates to ensure valid inference on primary outcomes. It addresses biases in proxy-based estimates through four diagnostic levels and targeted corrections, improving decision-making accuracy in experimentation, prevalence estimation, and monitoring across diverse domains.