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Cluster-based randomized experiments are popular designs for mitigating thebias of standard estimators when interference is present and classical causalinference and experimental design assumptions (such as SUTVA or ITR) do nothold. Without an exact knowledge of the interference structure, it can bechallenging to understand which partitioning of the experimental units isoptimal to minimize the estimation bias. In the paper, we introduce amonotonicity condition under which a novel two-stage experimental design allowsus to determine which of two cluster-based designs yields the least biasedestimator. We then consider the setting of online advertising auctions and showthat reserve price experiments verify the monotonicity condition and theproposed framework and methodology applies. We validate our findings on anadvertising auction dataset.

Optimizing cluster-based randomized experiments under a monotonicity assumption