Bet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile Forecasters

2026-07-14 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

A new distribution-free, game-theoretic framework enables continuous, feature-aware auditing of black-box conditional quantile forecasters, addressing limitations of fixed-horizon backtests and information-dependent calibration. Developed by Antonov et al., this framework formalizes conditional quantile calibration based on auditor information, identifying alternatives for which power can be achieved. It derives finite-time detection guarantees without i.i.d. assumptions. Empirical validation on simulated and real data, including Rossmann store-sales and Chronos-2 forecasts, demonstrates its ability to detect miscalibration missed by marginal audits, particularly concerning features like promotion status and Saturday.

Key takeaway

For MLOps engineers deploying black-box conditional quantile forecasters, you should implement continuous, feature-aware auditing using this framework. This approach ensures anytime-valid calibration monitoring, even with non-i.i.d. data, and provides interpretable diagnostics by identifying specific features causing miscalibration. Prioritize contextual betting strategies that adapt to your available feature dictionary to gain power against subtle forecast errors.

Key insights

A new framework enables continuous, feature-aware auditing of conditional quantile forecasts, addressing information asymmetry.

Principles

• Calibration depends on the auditor's information.
• Coarser audits are valid but can lack power.
• Validity transfers from coarser to richer nulls.

Method

The framework uses a sequential betting game, where an online learner adapts linear contextual bets over a predictable feature dictionary to detect miscalibration.

In practice

• Use contextual betting to detect feature-specific miscalibration.
• Monitor promotion status, Saturday, and log-price features.

Topics

• Conditional Quantile Forecasting
• Anytime-Valid Inference
• Feature-Aware Auditing
• Black-Box Models
• Online Convex Optimization
• Chronos-2

Best for: AI Scientist, Research Scientist, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.