Learning to target with network interference
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 th...
| Authors | Xiaomeng Wang, Hamsa Bastani, Osbert Bastani et al. |
|---|---|
| Primary category | stat.ML |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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