  We describe our framework, deployed at Facebook, that accounts for
interference between experimental units through cluster-randomized experiments.
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. We introduce a cluster-based regression adjustment that
substantially improves precision for estimating global treatment effects as
well as testing for interference as part of our estimation procedure. With this
regression adjustment, we find that imbalanced clusters can better account for
interference than balanced clusters without sacrificing accuracy. In addition,
we show how logging exposure to a treatment can be used for additional variance
reduction. Interference is a widely acknowledged issue with 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.
