A/B Testing in Dense Large-Scale Networks: Design and Inference
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Design of experiments and estimation of treatment effects in large-scale networks, in the presence of strong interference, is a challenging and important problem. Most existing methods' performance deteriorates as the density of the network increases. In this paper, we present a novel strategy for accurately estimating the causal effects of a class of treatments in a dense large-scale network. First, we design an approximate randomized controlled experiment, by solving an optimization problem to allocate treatments that mimic the competition effect. Then we apply an importance sampling adjustment to correct for the design bias in estimating treatment effects from experimental data. We provide theoretical guarantees, verify robustness in a simulation study, and validate the usefulness of our procedure in a real-world experiment.
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