Statistics and Data Science Seminar
Qiong Zhang
Clemson University
Statistical Designs for Network A/B Testing
Abstract: A/B testing is an effective method to assess the potential impact of two treatments. For A/B tests conducted by IT companies like Meta and LinkedIn, the test users can be connected and form a social network. Users’ responses may be influenced by their network connections, and the quality of the treatment estimator of an A/B test depends on how the two treatments are allocated across different users in the network. In this talk, I will discuss optimal design criteria based on some commonly used outcome models, under assumptions of network-correlated outcomes or network interference. I will show that the optimal design criteria under these network assumptions depend on several key statistics of the random design vector. I will discuss a framework to develop algorithms that generate rerandomization designs meeting the required conditions of those statistics. I further talk about asymptotic distributions to guide the specification of algorithmic parameters and validate the proposed approach using both synthetic and real-world networks.
Wednesday October 1, 2025 at 4:15 PM in Zoom