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Randomization Tests in Switchback Experiments

Jizhou Liu, Liang Zhong

arXiv 26 Feb 2026 · Statistics — Methodology

arXiv:2602.23257 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Switchback experiments--alternating treatment and control over time--are widely used when unit-level randomization is infeasible, outcomes are aggregated, or user interference is unavoidable. In practice, experimentation must support fast product cycles, so teams often run studies for limited durations and make decisions with modest samples. At the same time, outcomes in these time-indexed settings exhibit serial dependence, seasonality, and occasional heavy-tailed shocks, and temporal interference (carryover or anticipation) can render standard asymptotics and naive randomization tests unreliable. In this paper, we develop a randomization-test framework that delivers finite-sample valid, distribution-free p-values for several null hypotheses of interest using only the known assignment mechanism, without parametric assumptions on the outcome process. For causal effects of interests, we impose two primitive conditions--non-anticipation and a finite carryover horizon m--and construct conditional randomization tests (CRTs) based on an ex ante pooling of design blocks into "sections," which yields a tractable conditional assignment law and ensures imputability of focal outcomes. We provide diagnostics for learning the carryover window and assessing non-anticipation, and we introduce studentized CRTs for a session-wise weak null that accommodates within-session seasonality with asymptotic validity. Power approximations under distributed-lag effects with AR(1) noise guide design and analysis choices, and simulations demonstrate favorable size and power relative to common alternatives. Our framework extends naturally to other time-indexed designs.

Citation extraction

28
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Bojinov, Iavor and Simchi-Levi, David and Zhao, Jinglong (2023) Design and Analysis of Switchback Experiments1.000164100%
2Zhong, Liang (2024) Unconditional randomization tests for interference self1.00053100%
3Susan Athey and Dean Eckles and Guido W. Imbens (2018) Exact p-Values for Network Interference0.92843100%
4Basse, G W and Feller, A and Toulis, P (2019) Randomization tests of causal effects under interference0.84310360%
5Puelz, David and Basse, Guillaume and Feller, Avi and Toulis, Panos (2021) A Graph-Theoretic Approach to Randomization Tests of Causal Effects under General Interference0.73732100%
6Xinran Li and Peng Ding (2017) General Forms of Finite Population Central Limit Theorems with Applications to Causal Inference0.64422100%
7Jason Wu and Peng Ding (2021) Randomization Tests for Weak Null Hypotheses in Randomized Experiments0.64422100%
8Anqi Zhao and Peng Ding (2021) Covariate-adjusted Fisher randomization tests for the average treatment effect0.64422100%
9Zhichao Jiang and Peng Ding (2025) Principled analysis of crossover designs: causal effects, efficient estimation, and robust inference0.51121100%
10Kohavi, R. and Tang, D. and Xu, Y (2020) Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing0.51121100%

Showing the top 10 of 28 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

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1Randomization Tests in Randomized Saturation Designs0.40511