Sharan Sahu, Martin T. Wells · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29982 · open-access PDF
適応実験は、バンディットアルゴリズムを使用して、より高い期待される報酬を持つ介入を好みます。しかし、決定者は現在の治療効果についても信頼できる推論を必要とします:介入と参照腕間の差異は、歴史的な平均値ではなく、現在の時点で平均的な報酬です。これらの目標の対立:報酬最大化はより良い介入を好みますが、現在の効果推論は、結果が時間とともに変化するにつれて潜在的に不最適な介入の継続的な探求を必要とします。
Machine translation (Meta M2M100); the English original follows.
Adaptive experiments use bandit algorithms to favor interventions with higher expected rewards. However, decision-makers also require reliable inference on current treatment effects: differences between intervention and reference-arm mean rewards at the present time, rather than historical averages. These objectives conflict: reward maximization favors better interventions, whereas current-effect inference requires continued exploration of potentially suboptimal interventions as outcomes change over time. We characterize this tradeoff for $K$-arm bandits with Hölder-smooth mean rewards of expo
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