# Differences-in-Neighbors for Network Interference in Experiments

arXiv: https://arxiv.org/abs/2503.02271 · HTML: https://mlsanity.com/paper/2503.02271/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 20 of 20)

- [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)
- [Interference, Bias, and Variance in Two-Sided Marketplace Experimentation: Guidance for Platforms](https://mlsanity.com/paper/2104.12222/) (2021)
- [Markovian Interference in Experiments](https://mlsanity.com/paper/2206.02371/) (2022)
- [Multiple Randomization Designs: Estimation and Inference with Interference](https://mlsanity.com/paper/2112.13495/) (2021)
- [Randomized Graph Cluster Randomization](https://mlsanity.com/paper/2009.02297/) (2020)
- [Rate-Optimal Cluster-Randomized Designs for Spatial Interference](https://mlsanity.com/paper/2111.04219/) (2021)
- [Optimal Experimental Design for Staggered Rollouts](https://mlsanity.com/paper/1911.03764/) (2019)
- [Correcting for Interference in Experiments: A Case Study at Douyin](https://mlsanity.com/paper/2305.02542/) (2023)
- [Data-Driven Switchback Experiments: Theoretical Tradeoffs and Empirical Bayes Designs](https://mlsanity.com/paper/2406.06768/) (2024)
- [Network experimentation at scale](https://mlsanity.com/paper/2012.08591/) (2020)
- [Limiting Bias from Test-Control Interference in Online Marketplace Experiments](https://mlsanity.com/paper/2004.12162/) (2020)
- [Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design](https://mlsanity.com/paper/2208.05553/) (2022)
- [Causal Message Passing for Experiments with Unknown and General Network Interference](https://mlsanity.com/paper/2311.08340/) (2023)
- [Estimating Effects of Long-Term Treatments](https://mlsanity.com/paper/2308.08152/) (2023)
- [Clustered Switchback Designs for Experimentation Under Spatio-temporal Interference](https://mlsanity.com/paper/2312.15574/) (2023)
- [Low-order outcomes and clustered designs: combining design and analysis for causal inference under network interference](https://mlsanity.com/paper/2405.07979/) (2024)
- [Cluster-Randomized Trials with Cross-Cluster Interference](https://mlsanity.com/paper/2310.18836/) (2023)

## Built on by (most cited first, 1 of 1)

- [Causal Effects with Unobserved Unit Types in Interacting Human-AI Systems](https://mlsanity.com/paper/2603.01339/) (2026)

## Abstract (arXiv)

Experiments in online platforms frequently suffer from network interference, in which a treatment applied to a given unit affects outcomes for other units connected via the platform. This SUTVA violation biases naive approaches to experiment design and estimation. A common solution is to reduce interference by clustering connected units, and randomizing treatments at the cluster level, typically followed by estimation using one of two extremes: either a simple difference-in-means (DM) estimator, which ignores remaining interference; or an unbiased Horvitz-Thompson (HT) estimator, which eliminates interference at great cost in variance. Even combined with clustered designs, this presents a limited set of achievable bias variance tradeoffs. We propose a new estimator, dubbed Differences-in-Neighbors (DN), designed explicitly to mitigate network interference. Compared to DM estimators, DN achieves bias second order in the magnitude of the interference effect, while its variance is exponentially smaller than that of HT estimators. When combined with clustered designs, DN offers improved bias-variance tradeoffs not achievable by existing approaches. Empirical evaluations on a large-scale social network and a city-level ride-sharing simulator demonstrate the superior performance of DN in experiments at practical scale.