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arXiv · 2610.10223

Neutral Is Not Free: Evaluating Downside Risk in Neutral Launches

Pablo Alcain·Jason Kang·Mia Garrard·Marine Veits·Houssam Nassif·Abbas Zaidi
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Abstract

Evaluating "neutral launches" (e.g., infrastructure upgrades) using traditional confidence interval overlap is flawed: it is dangerously permissive with scarce data and excessively restrictive with abundant data. To resolve this, this paper introduces Expected Bayesian Loss (EBL), a continuous metric that quantifies both the probability and expected severity of metric degradation. Computable directly from standard frequentist estimates, EBL explicitly penalizes empirical noise and high-variance experiments. Validated against expert decisions, EBL provides experimentation platforms with a rigorous, tunable guardrail that aligns statistical safety with institutional risk appetite.

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Pablo Alcain, Jason Kang, Mia Garrard, Marine Veits, Houssam Nassif, Abbas Zaidi. 2026-10-07. Neutral Is Not Free: Evaluating Downside Risk in Neutral Launches. https://arxiv.org/abs/2610.10223

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