  The reliability of controlled experiments, or "A/B tests," can often be
compromised due to the phenomenon of network interference, wherein the outcome
for one unit is influenced by other units. To tackle this challenge, we propose
a machine learning-based method to identify and characterize heterogeneous
network interference. Our approach accounts for latent complex network
structures and automates the task of "exposure mapping'' determination, which
addresses the two major limitations in the existing literature. We introduce
"causal network motifs'' and employ transparent machine learning models to
establish the most suitable exposure mapping that reflects underlying network
interference patterns. Our method's efficacy has been validated through
simulations on two synthetic experiments and a real-world, large-scale test
involving 1-2 million Instagram users, outperforming conventional methods such
as design-based cluster randomization and analysis-based neighborhood exposure
mapping. Overall, our approach not only offers a comprehensive, automated
solution for managing network interference and improving the precision of A/B
testing results, but it also sheds light on users' mutual influence and aids in
the refinement of marketing strategies.

Machine Learning;Machine Learning;Social and Information Networks