Publication

Network Experimentation at Scale

Conference on Knowledge Discovery and Data Mining (SIGKDD)


Abstract

We describe our network experimentation framework, deployed at Facebook, which accounts for interference between experimental units. We document this system, including the design and estimation procedures, and detail insights we have gained from the many experiments that have used this system at scale. In our estimation procedure, we introduce a cluster-based regression adjustment that substantially improves precision for estimating global treatment effects, as well as a procedure to test for interference. With our regression adjustment, we find that imbalanced clusters can better account for interference than balanced clusters without sacrificing accuracy. In addition, we show that logging exposure to a treatment can result in additional variance reduction. Interference is a widely acknowledged issue in online field experiments, yet there is less evidence from real-world experiments demonstrating interference in online settings. We fill this gap by describing two case studies that capture significant network effects and highlight the value of this experimentation framework.

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