  Cluster-based randomized experiments are popular designs for mitigating the
bias of standard estimators when interference is present and classical causal
inference and experimental design assumptions (such as SUTVA or ITR) do not
hold. Without an exact knowledge of the interference structure, it can be
challenging to understand which partitioning of the experimental units is
optimal to minimize the estimation bias. In the paper, we introduce a
monotonicity condition under which a novel two-stage experimental design allows
us to determine which of two cluster-based designs yields the least biased
estimator. We then consider the setting of online advertising auctions and show
that reserve price experiments verify the monotonicity condition and the
proposed framework and methodology applies. We validate our findings on an
advertising auction dataset.
