Sharan Sahu, Martin T. Wells · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29982 · open-access PDF
适应性实验使用破坏性算法,以有利于具有更高预期回报的干预。然而,决策者还需要对当前治疗效果进行可靠的推断:干预和参考手臂之间的差异现在平均奖励,而不是历史平均值。这些目标冲突:奖励最大化有利于更好的干预,而当前效应推断需要随着时间的推移继续探索潜在不最佳的干预措施。
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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