Two methods are introduced to learn plug-in composite surrogates that maximize effect predictiveness, with the direct surrogate-effect modeling approach outperforming baselines on synthetic data with known effects and real-world experiment data.
surrogate index
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PROXIMA scores proxy reliability via a composite of effect correlation, directional accuracy, and segment fragility, achieving 98.4% decision agreement with an oracle on two public datasets.
Proposes an optimal blending framework for proxy and north star metrics in online A/B testing that adjusts decision weights based on statistical power and proxy quality.
citing papers explorer
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Learning plug-in surrogate endpoints for randomized experiments
Two methods are introduced to learn plug-in composite surrogates that maximize effect predictiveness, with the direct surrogate-effect modeling approach outperforming baselines on synthetic data with known effects and real-world experiment data.
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PROXIMA: A Reliability Scoring Framework for Proxy Metrics in Online Controlled Experiments
PROXIMA scores proxy reliability via a composite of effect correlation, directional accuracy, and segment fragility, achieving 98.4% decision agreement with an oracle on two public datasets.
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Blending Proxy Metrics with a North Star
Proposes an optimal blending framework for proxy and north star metrics in online A/B testing that adjusts decision weights based on statistical power and proxy quality.