Benefits and Costs of Adaptive Sampling
自适应抽样的收益与成本AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究多臂序贯实验中的处理分配与实验臂均值估计，直接关联因果实验设计。
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自适应抽样的收益与成本
- 研究问题
- 序贯实验中，自适应分配何时能比均匀抽样更精确地估计各实验臂均值，又如何平衡推断精度与分配到较差实验臂的成本？
- 方法与思路
- 作者分析按实验臂方差分配的自适应 Neyman 方案，并提出 SARP 和 NARP，在重视推断与重视遗憾的策略间调整探索。
- 材料陈述的贡献
- 摘要称，方法刻画严格均方误差改善的条件，所提策略以最优速率接近完全信息基准；模拟展示精度改善及实验性能损失控制。
- 阅读关注点
- 应核对有限样本改善阈值、方差学习成本、推断与遗憾联合目标的权重，以及最优速率对应的假设、基准和策略插值实现。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未给出改善条件、目标函数和速率公式，不能据此认定所有实验都应自适应抽样。
原文摘要
Multi-armed bandits are widely used for sequential experimentation in clinical trials, recommendation systems, and online platforms. While regret minimization and valid inference from adaptively collected data have each been studied extensively, a basic question remains: when does adaptivity \emph{improve estimation precision} relative to uniform designs, and how should inference be balanced against the online cost of experimentation? We first study arm-level mean estimation under mean-squared-error (MSE) objectives. We characterize when an adaptive Neyman allocation, which allocates samples according to arm variance, yields strict MSE improvements over uniform sampling. When there is variance heterogeneity across arms, these improvements arise at modest sample sizes, clarifying that adaptivity can be preferable for inference not only asymptotically, but also in many practical finite-sample settings. We then study a joint inference-regret objective that accounts for the cost of assigning units to inferior arms during experimentation. We propose the Static-Allocation Rate Policy (SARP) and Neyman-Adaptive Rate Policy (NARP), which interpolates between inference- and regret-oriented policies by adjusting exploration to the local structure of the instance. We show that SARP and NARP converge to the complete-information benchmark at the optimal rate as the sampling budget grows. Our proposed policies are practically attractive as it linearly interpolates between any standard regret-minimizing algorithm and inference-targeting adaptive policies. Yet we show it still enjoys the oracle-based asymptotic optimal rate. Simulations support the theory by demonstrating improved precision over uniform allocation while controlling performance loss across a range of instances.