Experimentation is central to modern digital businesses, but many operational decisions cannot be randomized at the user level. In such cases, cluster-level experiments, where clusters are usually geographic, come to the rescue. However, such experiments often suffer from low power due to persistent cluster heterogeneity, strong seasonality, and autocorrelated outcome metrics, as well as common shocks that move many clusters simultaneously. On an example of airline pricing - where policies are typically applied at the route level and thus the A/B test unit of analysis is a route - we study switchback designs to remedy these problems. In switchback designs, each cluster (route in our case) alternates between treatment and control on a fixed schedule, creating within-route contrasts that mitigate time-invariant heterogeneity and reduce sensitivity to low-frequency noise. We provide a unified Two-Way Fixed Effects interpretation of switchback experiments that makes the identifying variation explicit after partialling out route and time effects, clarifying how switching cadence interacts with temporal dependence to determine precision. Empirically, we evaluate weekly and daily switchback cadences using calibrated synthetic regimes and operational airline data from ancillary pricing. In our evaluations, switchbacks decrease standard errors by up to 67%, with daily switching yielding the largest gains over short horizons and weekly switching offering a strong and simpler-to-operationalize alternative.

翻译：实验是现代数字业务的核心，但许多运营决策无法在用户层面进行随机化。在此类情况下，通常以地理区域为单位的集群级实验便成为解决方案。然而，此类实验常因持续的集群异质性、强烈的季节性、自相关的结果指标以及同时影响多个集群的常见冲击而面临统计功效不足的问题。以航空公司定价为例——定价策略通常在航线层面实施，因此A/B测试的分析单元为航线——我们研究切换设计以解决这些问题。在切换设计中，每个集群（本文中指航线）按固定时间表在实验组与对照组之间交替切换，从而创建航线内的对比，这有助于消除时间不变的异质性并降低对低频噪声的敏感性。我们提出了切换实验的统一双向固定效应解释框架，该框架在剔除航线效应和时间效应后明确了识别变异的来源，阐明了切换频率如何与时间依赖性相互作用以决定实验精度。在实证层面，我们使用校准的合成机制和来自辅营定价的航空公司运营数据，评估了每周与每日切换频率的效果。评估结果表明，切换设计可将标准误差降低高达67%，其中每日切换在短期范围内收益最大，而每周切换则提供了强大且更易实施的替代方案。