  Randomized experiments, or "A/B" tests, remain the gold standard for
evaluating the causal effect of a policy intervention or product change.
However, experimental settings, such as social networks, where users are
interacting and influencing one another, may violate conventional assumptions
of no interference for credible causal inference. Existing solutions to the
network setting include accounting for the fraction or count of treated
neighbors in a user's network, yet most current methods do not account for the
local network structure beyond simply counting the number of neighbors. Our
study provides an approach that accounts for both the local structure in a
user's social network via motifs as well as the treatment assignment conditions
of neighbors. We propose a two-part approach. We first introduce and employ
"causal network motifs", which are network motifs that characterize the
assignment conditions in local ego networks; and then we propose a tree-based
algorithm for identifying different network interference conditions and
estimating their average potential outcomes. Our approach can account for
social network theories, such as structural diversity and echo chambers, and
also can help specify network interference conditions that are suitable to each
experiment. We test our method on a synthetic network setting and on a
real-world experiment on a large-scale network, which highlight how accounting
for local structures can better account for different interference patterns in
networks.
