数据驱动的轮换实验：理论权衡与经验贝叶斯设计
Data-Driven Switchback Experiments: Theoretical Tradeoffs and Empirical Bayes Designs
AI总结：
本文研究单聚合单元上轮换实验的设计与分析，提出经验贝叶斯设计方法，通过平衡周期性、控制切换频率和随机化区间端点，在真实网约车数据上将MSE降低33%。
摘要：
我们研究在单个聚合单元上进行的轮换实验的设计与分析。设计问题是将连续时间空间划分为若干区间，并在区间之间切换处理，以最小化处理效应的估计误差。我们表明，估计误差取决于四个因素：遗留效应、周期性、序列相关结果以及同时进行的实验的影响。我们推导出一个严格的偏差-方差分解，并展示这些因素对估计误差的权衡。该分解为选择设计提供了三个新见解：第一，在处理区间和对照区间之间平衡周期性可以降低方差；第二，较少切换可以降低来自遗留效应的偏差，但同时会增加来自相关结果的方差，反之亦然；第三，随机化区间的起点和终点可以同时降低来自同时实验的偏差和方差。结合这些见解，我们提出了一种新的经验贝叶斯设计方法。该方法利用先验数据和实验来设计未来的实验。我们使用来自一个网约车平台的真实数据说明了这一方法，与平台上使用的现状设计相比，该设计将均方误差（MSE）降低了33%。
英文摘要：
We study the design and analysis of switchback experiments conducted on a single aggregate unit. The design problem is to partition the continuous time space into intervals and switch treatments between intervals, in order to minimize the estimation error of the treatment effect. We show that the estimation error depends on four factors: carryover effects, periodicity, serially correlated outcomes, and impacts from simultaneous experiments. We derive a rigorous bias-variance decomposition and show the tradeoffs of the estimation error from these factors. The decomposition provides three new insights in choosing a design: First, balancing the periodicity between treated and control intervals reduces the variance; second, switching less frequently reduces the bias from carryover effects while increasing the variance from correlated outcomes, and vice versa; third, randomizing interval start and end points reduces both bias and variance from simultaneous experiments. Combining these insights, we propose a new empirical Bayes design approach. This approach uses prior data and experiments for designing future experiments. We illustrate this approach using real data from a ride-sharing platform, yielding a design that reduces MSE by 33% compared to the status quo design used on the platform.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
