  The reliability of controlled experiments, commonly referred to as "A/B
tests," is often compromised by network interference, where the outcomes of
individual units are influenced by interactions with others. Significant
challenges in this domain include the lack of accounting for complex social
network structures and the difficulty in suitably characterizing network
interference. To address these challenges, we propose a machine learning-based
method. We introduce "causal network motifs" and utilize transparent machine
learning models to characterize network interference patterns underlying an A/B
test on networks. Our method's performance has been demonstrated through
simulations on both a synthetic experiment and a large-scale test on Instagram.
Our experiments show that our approach outperforms conventional methods such as
design-based cluster randomization and conventional analysis-based neighborhood
exposure mapping. Our approach provides a comprehensive and automated solution
to address network interference for A/B testing practitioners. This aids in
informing strategic business decisions in areas such as marketing effectiveness
and product customization.
