A Thompson sampling algorithm jointly infers unknown network interference and learns optimal individual-level treatments, with sublinear regret for additive spillover models and an explore-then-commit variant for general neighborhood interference.
The design phase reuses the appendix count-based NIA sweep at n= 20 (Appendix E.8); only the inference-phase configuration is reported here
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Adaptive Policy Learning Under Unknown Network Interference
A Thompson sampling algorithm jointly infers unknown network interference and learns optimal individual-level treatments, with sublinear regret for additive spillover models and an explore-then-commit variant for general neighborhood interference.