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.

提出了一种基于机器学习的方法来识别和表征异质网络干扰，该方法通过引入因果网络模式和透明机器学习模型来建立反映潜在网络干扰模式的最适 exposure mapping，为管理网络干扰和提高 A/B 测试精确性提供了全面的自动化解决方案。