generated from small-world models and then degree-corrected blockmodels.

We find that graph cluster randomization is capable of dramatically reducing bias compared to independent assignment without adding “too much” variance. The benefits of graph cluster randomization are larger when the network has more local clustering and when social interactions are strong. If social interactions are weak or the network has little local clustering, then the benefits of the more complex graph-clustered design are reduced. Finally, we found larger bias and error reductions through design than analysis: analysis strategies using neighborhood-based definitions of effective treatments does further reduce bias, but often at a substantial cost to precision such that the simple estimators were preferable in terms of error. No combination of design and analysis is expected to work well across very different situations, but these general insights from simulation can be a guide to practical real-world experimentation in the presence of peer effects. Furthermore, by identifying sufficient conditions for bias reduction, we can understand when design and analysis changes will at least not increase bias.

Related work
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Much of the literature on interference between units focuses on situations where there are multiple independent groups, such that there are interactions within, but 