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A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Optimal Experiments for Partial Causal Effect Identification Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Perturbation is All You Need for Extrapolating Language Models Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization Realizable Bayes-Consistency for General Metric Losses Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Segmenting Human-LLM Co-authored Text via Change Point Detection Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint Robust and Fast Training via Per-Sample Clipping
Blending Proxy Metrics with a North Star
[Submitted on 19 Jun 2026] · 2026-06-23 · via stat updates on arXiv.org

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Abstract:Proxy metrics are widely used to improve the precision and velocity of online experimentation (aka A/B testing). Although proxies are often motivated by long-term outcomes that the experimenter does not observe, in many settings they are used alongside a contemporaneous but statistically insensitive north star. This can lead to a practical dilemma: when should experimenters trust the proxy metric, and when should they trust the north star? In this paper, I propose an optimal blending approach that smoothly guides decision-making towards the north star as the power of the experiment increases and away from the north star as the quality of the proxy metric improves. I study the implications of this decision-making framework for the design of experiments and of experimentation programs. Equipped with better (worse) proxy metrics, experimenters should run smaller and more (larger and fewer) experiments. I show how to leverage past experiments to estimate optimal blending weights and experiment sizes. Lastly, I describe the real-world application of the methodology to an experimentation program at Netflix.

Submission history

From: Winston Chou [view email]
[v1] Fri, 19 Jun 2026 21:02:24 UTC (1,772 KB)