In this article, we discuss an approach to the design of experiments in a network.
In particular, we describe a method to prevent potential contamination (or
inconsistent treatment exposure) of samples due to network effects. We present data
from Google Cloud Platform (GCP) as an example of how we use A/B testing when users
are connected. Our methodology can be extended to other areas where the network is
observed and when avoiding contamination is of primary concern in experiment
design. We first describe the unique challenges in designing experiments on
developers working on GCP. We then use simulation to show how proper selection of
the randomization unit can avoid estimation bias. This simulation is based on the
actual user network of GCP.