Adaptive Policy Learning Under Unknown Network Interference
Adaptive experimentation under unknown network interference requires solving two coupled problems: (i) learning the underlying dynamics of interference among units and (ii) using these dynamics to inform treatment allocation in order to maximize a cumulative outcome of interest (e.g. revenue). Existing adaptive experimentation methods either assume the interference network is fully known or bypass the network by operating on coarse cluster-level randomizations. We develop a Thompson sampling algorithm that jointly learns the interference network and adaptively optimizes individual-level treatment allocations via a Gibbs sampler. The algorithm returns both an optimized treatment policy and...
| Authors | Aidan Gleich, Eric Laber, Alexander Volfovsky |
|---|---|
| Primary category | stat.ML |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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