Randomization Tests in Switchback Experiments

Switchback experiments assign an experimental unit, such as a market or a platform, to treatment or control over successive blocks of time. Inference can be challenging because these experiments often contain only a small number of randomized blocks, while outcomes may exhibit serial dependence, seasonality, and treatment effects that persist across periods. We develop a conditional randomization test for the null of no total treatment effect that is finite-sample valid under standard assumptions on temporal interference without requiring a parametric model for outcomes. We also develop randomization tests for these assumptions: a carryover test that assesses whether past assignments continue to affect outcomes beyond a prespecified horizon and a non-anticipation test that assesses whether future assignments affect current outcomes. For hypotheses about average treatment effects, we establish asymptotic validity of studentized randomization tests under additional regularity conditions. Finally, we derive power approximations and characterize the tradeoffs between experimental design choices, the number of informative randomized comparisons, and statistical power. Numerical experiments with stylized potential outcomes and a dynamic rideshare model illustrate finite-sample performance and implications for experimental design.