# Clustered Switchback Designs for Experimentation Under Spatio-temporal Interference

arXiv: https://arxiv.org/abs/2312.15574 · HTML: https://mlsanity.com/paper/2312.15574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 36 references that are in the map.
- First posted on arXiv 25 Dec 2023.

## Builds on (most cited first, 22 of 22)

- [Doubly Robust Off-policy Value Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1511.03722/) (2015)
- [Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1604.00923/) (2016)
- [Identification and estimation of treatment and interference effects in observational studies on networks](https://mlsanity.com/paper/1609.06245/) (2016)
- [Graph cluster randomization: network exposure to multiple universes](https://mlsanity.com/paper/1305.6979/) (2013)
- [Design and analysis of experiments in networks: Reducing bias from interference](https://mlsanity.com/paper/1404.7530/) (2014)
- [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)
- [Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework](https://mlsanity.com/paper/2002.01711/) (2020)
- [Markovian Interference in Experiments](https://mlsanity.com/paper/2206.02371/) (2022)
- [Switchback Experiments under Geometric Mixing](https://mlsanity.com/paper/2209.00197/) (2022)
- [Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach](https://mlsanity.com/paper/2006.05591/) (2020)
- [Randomized Graph Cluster Randomization](https://mlsanity.com/paper/2009.02297/) (2020)
- [Causal inference with misspecified exposure mappings: separating definitions and assumptions](https://mlsanity.com/paper/2103.06471/) (2021)
- [Rate-Optimal Cluster-Randomized Designs for Spatial Interference](https://mlsanity.com/paper/2111.04219/) (2021)
- [Elements of estimation theory for causal effects in the presence of network interference](https://mlsanity.com/paper/1702.03578/) (2017)
- [Regression adjustments for estimating the global treatment effect in experiments with interference](https://mlsanity.com/paper/1808.08683/) (2018)
- [Model-assisted design of experiments in the presence of network correlated outcomes](https://mlsanity.com/paper/1507.00803/) (2015)
- [Network Interference in Micro-Randomized Trials](https://mlsanity.com/paper/2202.05356/) (2022)
- [Network Cluster-Robust Inference](https://mlsanity.com/paper/2103.01470/) (2021)
- [Experimenting under Stochastic Congestion](https://mlsanity.com/paper/2302.12093/) (2023)
- [Causal Inference under Network Interference with Noise](https://mlsanity.com/paper/2105.04518/) (2021)
- [Causal Inference in Longitudinal Data under Unknown Interference](https://mlsanity.com/paper/2106.15074/) (2021)

## Built on by (most cited first, 3 of 3)

- [Multi-Armed Bandits with Interference](https://mlsanity.com/paper/2402.01845/) (2024)
- [Differences-in-Neighbors for Network Interference in Experiments](https://mlsanity.com/paper/2503.02271/) (2025)
- [On Evolution-Based Models for Experimentation Under Interference](https://mlsanity.com/paper/2511.21675/) (2025)

## Abstract (arXiv)

We consider experimentation in the presence of non-stationarity, inter-unit (spatial) interference, and carry-over effects (temporal interference), where we wish to estimate the global average treatment effect (GATE), the difference between average outcomes having exposed all units at all times to treatment or to control. We suppose spatial interference is described by a graph, where a unit's outcome depends on its neighborhood's treatments, and that temporal interference is described by an MDP, where the transition kernel under either treatment (action) satisfies a rapid mixing condition. We propose a clustered switchback design, where units are grouped into clusters and time steps are grouped into blocks, and each whole cluster-block combination is assigned a single random treatment. Under this design, we show that for graphs that admit good clustering, a truncated Horvitz-Thompson estimator achieves a tilde O(1/NT) mean squared error (MSE), matching the lower bound up to logarithmic terms for sparse graphs. Our results simultaneously generalize the results from citethu2022switchback,ugander2013graph and citetleung2022rate. Simulation studies validate the favorable performance of our approach.