A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing
This provides an automated solution for A/B testing practitioners to improve decision-making in areas like marketing and product customization, though it appears incremental as it builds on existing methods.
The paper tackles the problem of network interference compromising A/B test reliability by proposing a machine learning method using causal network motifs, which outperforms conventional methods like cluster randomization and exposure mapping in simulations on synthetic and Instagram data.
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.