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New research proposes sample splitting and shrinkage for incentive alignment in online experimentation

By PulseAugur Editorial · [1 sources] ·

A new paper on arXiv proposes practical mechanisms to align incentives in online experimentation, addressing the principal-agent conflict where experimenters may be rewarded for biased empirical average treatment effects. The research suggests that sample splitting and shrinkage can effectively bridge this gap, with sample splitting achieving perfect alignment at a bounded traffic cost. Shrinkage offers an alternative that requires no additional traffic and ensures interventions with negative expected effects are unprofitable. AI

IMPACT Addresses a core problem in the deployment and evaluation of AI features in online platforms.

RANK_REASON Academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research proposes sample splitting and shrinkage for incentive alignment in online experimentation

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Academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ermis Soumalias, Richard Mudd, Abbas Zaidi ·

    Incentive Alignment in Online Experimentation

    arXiv:2610.05922v2 Announce Type: replace-cross Abstract: Evaluating the causal effect of new features is a central goal for online platforms. While recent literature addresses limited testing traffic via centralized portfolio optimization, this perspective abstracts away a criti…

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