  The issue of missing network links in partially observed networks is
frequently neglected in empirical studies. This paper aims to address this
issue when investigating spillovers of program benefits in the presence of
network interactions. We focus on a nonparametric outcome model that allows for
flexible forms of heterogeneity, and we propose two methods to point identify
the treatment and spillover effects in the case of bounded network degree. When
the network degree is unbounded, these methods can be used to mitigate the bias
caused by missing network links. The first method utilizes two
easily-constructed network measures based on the incoming and outgoing links.
The second method relies on a single network measure with a known rate of
missing links. We demonstrate the effective bias reduction provided by our
methods using Monte Carlo experiments and a naturalistic simulation on
real-world school network data. Finally, we re-examine the spillovers of home
computer use on children's self-empowered learning.
