  Interference is ubiquitous when conducting causal experiments over networks.
Except for certain network structures, causal inference on the network in the
presence of interference is difficult due to the entanglement between the
treatment assignments and the interference levels. In this article, we conduct
causal inference under interference on an observed, sparse but connected
network, and we propose a novel design of experiments based on an independent
set. Compared to conventional designs, the independent-set design focuses on an
independent subset of data and controls their interference exposures through
the assignments to the rest (auxiliary set). We provide a lower bound on the
size of the independent set from a greedy algorithm , and justify the
theoretical performance of estimators under the proposed design. Our approach
is capable of estimating both spillover effects and treatment effects. We
justify its superiority over conventional methods and illustrate the empirical
performance through simulations.
