  Current approaches to A/B testing in networks focus on limiting interference,
the concern that treatment effects can "spill over" from treatment nodes to
control nodes and lead to biased causal effect estimation. Prominent methods
for network experiment design rely on two-stage randomization, in which
sparsely-connected clusters are identified and cluster randomization dictates
the node assignment to treatment and control. Here, we show that cluster
randomization does not ensure sufficient node randomization and it can lead to
selection bias in which treatment and control nodes represent different
populations of users. To address this problem, we propose a principled
framework for network experiment design which jointly minimizes interference
and selection bias. We introduce the concepts of edge spillover probability and
cluster matching and demonstrate their importance for designing network A/B
testing. Our experiments on a number of real-world datasets show that our
proposed framework leads to significantly lower error in causal effect
estimation than existing solutions.
