  We propose a matching method that recovers direct treatment effects from
randomized experiments where units are connected in an observed network, and
units that share edges can potentially influence each others' outcomes.
Traditional treatment effect estimators for randomized experiments are biased
and error prone in this setting. Our method matches units almost exactly on
counts of unique subgraphs within their neighborhood graphs. The matches that
we construct are interpretable and high-quality. Our method can be extended
easily to accommodate additional unit-level covariate information. We show
empirically that our method performs better than other existing methodologies
for this problem, while producing meaningful, interpretable results.
