  A network effect is said to take place when a new feature not only impacts
the people who receive it, but also other users of the platform, like their
connections or the people who follow them. This very common phenomenon violates
the fundamental assumption underpinning nearly all enterprise experimentation
systems, the stable unit treatment value assumption (SUTVA). When this
assumption is broken, a typical experimentation platform, which relies on
Bernoulli randomization for assignment and two-sample t-test for assessment of
significance, will not only fail to account for the network effect, but
potentially give highly biased results.
  This paper outlines a simple and scalable solution to measuring network
effects, using ego-network randomization, where a cluster is comprised of an
"ego" (a focal individual), and her "alters" (the individuals she is
immediately connected to). Our approach aims at maintaining representativity of
clusters, avoiding strong modeling assumption, and significantly increasing
power compared to traditional cluster-based randomization. In particular, it
does not require product-specific experiment design, or high levels of
investment from engineering teams, and does not require any changes to
experimentation and analysis platforms, as it only requires assigning treatment
an individual level. Each user either has the feature or does not, and no
complex manipulation of interactions between users is needed. It focuses on
measuring the one-out network effect (i.e the effect of my immediate
connection's treatment on me), and gives reasonable estimates at a very low
setup cost, allowing us to run such experiments dozens of times a year.
