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New framework aids online experiment design under unknown interference

By PulseAugur Editorial · [2 sources] ·

A new research paper introduces a framework for selecting online experiment designs when the mechanism of interference is unknown. The proposed method, called robust design selection, evaluates six different designs based on worst-case planning risk, considering factors like bias, variance, cost, and estimand mismatch. The paper provides theoretical guarantees and demonstrates its application on public datasets, recommending specific designs for Criteo ads, Open Bandit, and KuaiRand based on their respective risks. AI

RANK_REASON The cluster contains a research paper published on arXiv detailing a new framework for experiment design.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework aids online experiment design under unknown interference

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Prashant Shekhar, Caroline Howard ·

    Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems

    arXiv:2605.25290v1 Announce Type: cross Abstract: Online experiments in ads, recommendation, and member-experience systems are often planned before the dominant interference mechanism is known. A treatment may propagate through budgets, inventory, producer exposure, graph spillov…

  2. arXiv stat.ML TIER_1 English(EN) · Caroline Howard ·

    Choosing Online Experiment Designs under Interference in Ads, Recommendations, and Member-Experience Systems

    Online experiments in ads, recommendation, and member-experience systems are often planned before the dominant interference mechanism is known. A treatment may propagate through budgets, inventory, producer exposure, graph spillovers, or temporal carryover, making the randomizati…

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