# Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments

arXiv: https://arxiv.org/abs/2403.17285 · HTML: https://mlsanity.com/paper/2403.17285/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 73 of its 139 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 30 of 73)

- [Provably Efficient Reinforcement Learning with Linear Function Approximation](https://mlsanity.com/paper/1907.05388/) (2019)
- [Doubly Robust Off-policy Value Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1511.03722/) (2015)
- [Information-Theoretic Considerations in Batch Reinforcement Learning](https://mlsanity.com/paper/1905.00360/) (2019)
- [Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1604.00923/) (2016)
- [DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections](https://mlsanity.com/paper/1906.04733/) (2019)
- [Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation](https://mlsanity.com/paper/1810.12429/) (2018)
- [A Theoretical Analysis of Deep Q-Learning](https://mlsanity.com/paper/1901.00137/) (2019)
- [Minimax Weight and Q-Function Learning for Off-Policy Evaluation](https://mlsanity.com/paper/1910.12809/) (2019)
- [Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling](https://mlsanity.com/paper/1906.03393/) (2019)
- [Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes](https://mlsanity.com/paper/1908.08526/) (2019)
- [Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design](https://mlsanity.com/paper/2103.02438/) (2021)
- [A Review of Off-Policy Evaluation in Reinforcement Learning](https://mlsanity.com/paper/2212.06355/) (2022)
- [Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning](https://mlsanity.com/paper/1909.05850/) (2019)
- [Batch Policy Learning in Average Reward Markov Decision Processes](https://mlsanity.com/paper/2007.11771/) (2020)
- [Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning](https://mlsanity.com/paper/1611.03531/) (2016)
- [Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon Settings](https://mlsanity.com/paper/2001.04515/) (2020)
- [CoinDICE: Off-Policy Confidence Interval Estimation](https://mlsanity.com/paper/2010.11652/) (2020)
- [Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning](https://mlsanity.com/paper/2001.10742/) (2020)
- [Optimizing Sequential Experimental Design with Deep Reinforcement Learning](https://mlsanity.com/paper/2202.00821/) (2022)
- [A Deep Value-network Based Approach for Multi-Driver Order Dispatching](https://mlsanity.com/paper/2106.04493/) (2021)
- [Graph cluster randomization: network exposure to multiple universes](https://mlsanity.com/paper/1305.6979/) (2013)
- [Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health](https://mlsanity.com/paper/1912.13088/) (2019)
- [Bootstrapping Fitted Q-Evaluation for Off-Policy Inference](https://mlsanity.com/paper/2102.03607/) (2021)
- [Experimental Design in Two-Sided Platforms: An Analysis of Bias](https://mlsanity.com/paper/2002.05670/) (2020)
- [Design and Analysis of Switchback Experiments](https://mlsanity.com/paper/2009.00148/) (2020)
- [Anytime-valid off-policy inference for contextual bandits](https://mlsanity.com/paper/2210.10768/) (2022)
- [On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation](https://mlsanity.com/paper/2201.06169/) (2022)
- [Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process](https://mlsanity.com/paper/2202.10589/) (2022)
- [Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making](https://mlsanity.com/paper/2002.01751/) (2020)
- [Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework](https://mlsanity.com/paper/2002.01711/) (2020)

## Built on by (most cited first, 6 of 6)

- [Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach](https://mlsanity.com/paper/2505.20130/) (2025)
- [Statistical Inference in Reinforcement Learning: A Selective Survey](https://mlsanity.com/paper/2502.16195/) (2025)
- [A Two-armed Bandit Framework for A/B Testing](https://mlsanity.com/paper/2507.18118/) (2025)
- [Demystifying the Paradox of Importance Sampling with an Estimated History-Dependent Behavior Policy in Off-Policy Evaluation](https://mlsanity.com/paper/2505.22492/) (2025)
- [Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning](https://mlsanity.com/paper/2602.01853/) (2026)
- [Robust Sequential Experimental Design for A/B Testing](https://mlsanity.com/paper/2605.12899/) (2026)

## Abstract (arXiv)

A/B testing has become the gold standard for policy evaluation in modern technological industries. Motivated by the widespread use of switchback experiments in A/B testing, this paper conducts a comprehensive comparative analysis of various switchback designs in Markovian environments. Unlike many existing works which derive the optimal design based on specific and relatively simple estimators, our analysis covers a range of state-of-the-art estimators developed in the reinforcement learning (RL) literature. It reveals that the effectiveness of different switchback designs depends crucially on (i) the size of the carryover effect and (ii) the auto-correlations among reward errors over time. Meanwhile, these findings are estimator-agnostic, i.e., they apply to most RL estimators. Based on these insights, we provide a workflow to offer guidelines for practitioners on designing switchback experiments in A/B testing.