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.
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Ego-cluster randomization with model-based estimators enables consistent and asymptotically normal estimation of global treatment effects and spillovers in networks with interference.
citing papers explorer
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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.
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Estimating Treatment and Spillover Effects with the Ego-Cluster Experimental Design
Ego-cluster randomization with model-based estimators enables consistent and asymptotically normal estimation of global treatment effects and spillovers in networks with interference.