# Learning to target with network interference

arXiv: https://arxiv.org/abs/2605.27794 · HTML: https://mlsanity.com/paper/2605.27794/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 21 of 21)

- [An Introduction to Matrix Concentration Inequalities](https://mlsanity.com/paper/1501.01571/) (2015)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [High-Dimensional Sparse Linear Bandits](https://mlsanity.com/paper/2011.04020/) (2020)
- [Doubly-Robust Lasso Bandit](https://mlsanity.com/paper/1907.11362/) (2019)
- [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 Batch Learning in High-Dimensional Sparse Linear Contextual Bandits](https://mlsanity.com/paper/2008.11918/) (2020)
- [Interference, Bias, and Variance in Two-Sided Marketplace Experimentation: Guidance for Platforms](https://mlsanity.com/paper/2104.12222/) (2021)
- [Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structure](https://mlsanity.com/paper/2205.12803/) (2022)
- [Elements of estimation theory for causal effects in the presence of network interference](https://mlsanity.com/paper/1702.03578/) (2017)
- [Policy Targeting under Network Interference](https://mlsanity.com/paper/1906.10258/) (2019)
- [Average Direct and Indirect Causal Effects under Interference](https://mlsanity.com/paper/2104.03802/) (2021)
- [Multi-Armed Bandits with Interference](https://mlsanity.com/paper/2402.01845/) (2024)
- [Online Experimental Design With Estimation-Regret Trade-off Under Network Interference](https://mlsanity.com/paper/2412.03727/) (2024)
- [Linear Contextual Bandits with Interference](https://mlsanity.com/paper/2409.15682/) (2024)
- [Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference](https://mlsanity.com/paper/2503.07555/) (2025)
- [Scalable Policy Maximization Under Network Interference](https://mlsanity.com/paper/2505.18118/) (2025)
- [Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inference](https://mlsanity.com/paper/2510.07646/) (2025)
- [Causal Inference on Networks under Misspecified Exposure Mappings: A Partial Identification Framework](https://mlsanity.com/paper/2602.03459/) (2026)

## Abstract (arXiv)

This paper studies adaptive targeting under network interference in a bandit setting, where treatments applied to one individual may affect others through spillover effects. We consider a linear model in a sparse regime, where each individual's outcome can be affected by at most a few others. We first establish a regret lower bound showing that ignoring the network structure and reducing the problem to a standard linear bandit inevitably leads to inefficient learning, particularly in large populations. To understand how structural information can be leveraged, we analyze regimes with varying levels of knowledge of the interference structure: (1) full support knowledge, (2) knowledge of the column support sizes, and (3) no prior knowledge. For each regime, we establish regret lower bounds characterizing the fundamental limits of learning, and develop algorithms that achieve near-optimal regret. Together, our results provide a unified view of how knowledge of the interference structure governs the efficiency of online learning under interference, and offer practical adaptive targeting algorithms in each setting. Numerical experiments on synthetic and real-world data demonstrate the practical benefits of our algorithms.