  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
in the presence of competition among neighboring nodes. Then we apply an
importance sampling adjustment to correct for any leftover bias (from the
approximation) in estimating average treatment effects. We provide theoretical
guarantees, verify robustness in a simulation study, and validate the
scalability and usefulness of our procedure in a real-world experiment on a
large social network.
