# Switchback Experiments under Geometric Mixing

arXiv: https://arxiv.org/abs/2209.00197 · HTML: https://mlsanity.com/paper/2209.00197/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2022.

## Built on by (most cited first, 17 of 17)

- [Data-Driven Switchback Experiments: Theoretical Tradeoffs and Empirical Bayes Designs](https://mlsanity.com/paper/2406.06768/) (2024)
- [Multi-Armed Bandits with Interference](https://mlsanity.com/paper/2402.01845/) (2024)
- [Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing](https://mlsanity.com/paper/2305.10187/) (2023)
- [Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments](https://mlsanity.com/paper/2403.17285/) (2024)
- [Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach](https://mlsanity.com/paper/2505.20130/) (2025)
- [Off-policy Evaluation with Deeply-abstracted States](https://mlsanity.com/paper/2406.19531/) (2024)
- [Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?](https://mlsanity.com/paper/2507.11891/) (2025)
- [Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach](https://mlsanity.com/paper/2310.17496/) (2023)
- [Clustered Switchback Designs for Experimentation Under Spatio-temporal Interference](https://mlsanity.com/paper/2312.15574/) (2023)
- [Simulating and Experimenting with Social Media Mobilization Using LLM Agents](https://mlsanity.com/paper/2510.26494/) (2025)
- [Stochastic Gradient Descent with Adaptive Data](https://mlsanity.com/paper/2410.01195/) (2024)
- [On Evolution-Based Models for Experimentation Under Interference](https://mlsanity.com/paper/2511.21675/) (2025)
- [Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning](https://mlsanity.com/paper/2602.01853/) (2026)
- [Design Experiments to Compare Multi-armed Bandit Algorithms](https://mlsanity.com/paper/2603.05919/) (2026)
- [Validation of massively-parallel adaptive testing using dynamic control matching](https://mlsanity.com/paper/2305.01334/) (2023)
- [Causal Effects with Unobserved Unit Types in Interacting Human-AI Systems](https://mlsanity.com/paper/2603.01339/) (2026)
- [Robust Sequential Experimental Design for A/B Testing](https://mlsanity.com/paper/2605.12899/) (2026)

## 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.