mlsanityData to 7 Oct 2026

Switchback Experiments under Geometric Mixing

arXiv 2209.00197 ↗First posted 1 Sep 2022stat.ME econ.EMcited by 17

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Abstract (arXiv)

The switchback is an experimental design that measures treatment effects by repeatedly turning an intervention on and off for a whole system. Switchback experiments are a robust way to overcome cross-unit spillover effects; however, they are vulnerable to bias from temporal carryovers. In this paper, we consider properties of switchback experiments in Markovian systems that mix at a geometric rate. We find that, in this setting, standard switchback designs suffer considerably from carryover bias: Their estimation error decays as T-1/3 in terms of the experiment horizon T, whereas in the absence of carryovers a faster rate of T-1/2 would have been possible. We also show, however, that judicious use of burn-in periods can considerably improve the situation, and enables errors that decay as log(T)1/2T-1/2. Our formal results are mirrored in an empirical evaluation.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2022.

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Citing papers per year: 2023: 4; 2024: 5; 2025: 4; 2026: 4.

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  1. 2024Data-Driven Switchback Experiments: Theoretical Tradeoffs and Empirical Bayes Designscited by 11
  2. 2024Multi-Armed Bandits with Interferencecited by 10
  3. 2023Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharingcited by 7
  4. 2024Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experimentscited by 6
  5. 2025Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approachcited by 5
  6. 2025Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?cited by 4
  7. 2023Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approachcited by 3
  8. 2023Clustered Switchback Designs for Experimentation Under Spatio-temporal Interferencecited by 3
  9. 2025Simulating and Experimenting with Social Media Mobilization Using LLM Agentscited by 3
  10. 2024Off-policy Evaluation with Deeply-abstracted Statescited by 2
  11. 2024Stochastic Gradient Descent with Adaptive Datacited by 2
  12. 2025On Evolution-Based Models for Experimentation Under Interferencecited by 1
5 more
  1. 2026Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learningcited by 1
  2. 2026Design Experiments to Compare Multi-armed Bandit Algorithmscited by 1
  3. 2023Validation of massively-parallel adaptive testing using dynamic control matchingcited by 0
  4. 2026Causal Effects with Unobserved Unit Types in Interacting Human-AI Systemscited by 0
  5. 2026Robust Sequential Experimental Design for A/B Testingcited by 0