标题： 在数据共享下选择更好的老虎机算法：A/B测试何时有效？ 显示英文标题

标题： Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?

摘要： 我们研究设计用于比较两种推荐算法性能的A/B实验。 先前的研究表明，由于实验单元之间存在一种特定形式的干扰，标准的均值差异估计器在估计全局处理效应（GTE）时存在偏差。 具体来说，处于处理和对照算法下的单元会贡献到一个共享的数据池，该数据池随后用于训练两种算法，从而导致两组之间的干扰。 这种数据共享引起的偏差被称为“共生偏差”。 在本文中，我们强调，在决策过程中，选择更好的算法时，GTE的符号通常比其精确大小更重要。 我们在多臂老虎机框架下正式阐述了这一见解，并理论分析了在数据共享下预期GTE估计的符号何时与真实GTE的符号一致或矛盾。 我们的分析确定了探索与利用的水平是决定共生偏差如何影响算法选择的关键因素。 显示英文摘要

摘要： We study A/B experiments that are designed to compare the performance of two recommendation algorithms. Prior work has shown that the standard difference-in-means estimator is biased in estimating the global treatment effect (GTE) due to a particular form of interference between experimental units. Specifically, units under the treatment and control algorithms contribute to a shared pool of data that subsequently train both algorithms, resulting in interference between the two groups. The bias arising from this type of data sharing is known as "symbiosis bias". In this paper, we highlight that, for decision-making purposes, the sign of the GTE often matters more than its precise magnitude when selecting the better algorithm. We formalize this insight under a multi-armed bandit framework and theoretically characterize when the sign of the expected GTE estimate under data sharing aligns with or contradicts the sign of the true GTE. Our analysis identifies the level of exploration versus exploitation as a key determinant of how symbiosis bias impacts algorithm selection.