Multi-Metric Adaptive Experimental Design Under a Fixed Budget with Validation

2026-07-13 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

A new fixed-budget multi-metric Adaptive Experimental Design (AED) framework, featuring a two-phase exploration-validation structure, addresses challenges in online A/B testing with numerous candidates and diverse metrics. The framework introduces SHRVar, a Sequential Halving algorithm that generalizes existing methods by incorporating novel relative-variance-based sampling and an elimination strategy based on reward z values. This approach aims to identify the "best" treatment by maximizing the minimum probability of passing validation across all metrics, such as revenue and safety, even with heterogeneous variances. Numerical experiments confirm SHRVar's superior performance in exploration accuracy and validation success probability, demonstrating its robustness to varying numbers of treatments and unknown variances, while maintaining a lower Type I error in standard scenarios.

Key takeaway

For Machine Learning Engineers designing online experiments with numerous treatments and multiple critical metrics, adopting a two-phase adaptive design is crucial. SHRVar's relative-variance-based sampling and z-value elimination significantly enhance the probability of validating truly superior treatments while minimizing Type I errors. Implement this robust framework to efficiently identify and deploy new features, ensuring performance across all key metrics, even with heterogeneous variances.

Key insights

SHRVar integrates adaptive exploration and A/B validation for multi-metric online experiments, optimizing for robust treatment identification via relative-variance sampling.

Principles

• Multi-metric A/B tests need adaptive exploration for power and validation for inference.
• Robust treatment selection maximizes minimum validation probability across all metrics.
• Sampling should equalize z value estimate variance for efficient elimination.

Method

The framework employs a two-phase structure: an adaptive exploration phase identifies the best treatment, followed by an A/B test validation phase to verify quality and infer statistics using z values.

In practice

• Adopt z values for treatment evaluation in multi-metric online experiments.
• Prioritize relative-variance-based sampling for efficient resource allocation.
• Structure experiments with distinct exploration and validation phases.

Topics

• Multi-Armed Bandits
• Adaptive Experimental Design
• A/B Testing
• SHRVar Algorithm
• Z-value Statistics
• Heterogeneous Variance

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Data Scientist

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