Switchback experiments: Overview and considerations

Switchback experiments are an effective alternative to traditional A/B tests in scenarios where network effects, such as those in two-sided marketplaces like ridesharing services, make independent test and control groups impractical. These experiments alternate between test and control treatments based on time intervals rather than splitting the population, ensuring that everyone in the network receives the same treatment at any given time. This approach helps mitigate biases caused by network effects and is particularly suitable for analyzing transactional user behaviors within a single session. Implementing a switchback test requires careful consideration of time intervals, which should be long enough to capture desired effects but short enough to allow multiple test and control samples, as well as the use of independent clusters to enhance data sampling. The analysis of switchback experiments often involves regression or bootstrapping methods instead of standard t-tests to account for dependencies in the data. While switchback tests are not ideal for measuring long-term effects, they provide valuable insights into short-term user interactions when appropriately designed with domain-specific knowledge.