  Social influence, sometimes referred to as spillover or contagion, have been
extensively studied in various empirical social network research. However,
there are various estimation challenges in identifying social influence
effects, as they are often entangled with other factors, such as homophily in
the selection process, the individual's preference for the same social
settings, etc. Methods currently available either do not solve these problems
or require strong assumptions. Recent works by Xu 2018 and others show that a
latent-space adjusted approach based on the latent space model has potential to
disentangle the influence from other processes, and the simulation evidence
shows the approach performs better than other state-of-the-art approaches in
terms of recovering the true social influence effect when there is an
unobserved trait co-determining influence and selection. In this paper we
illustrate how latent space adjusted approach accounts for bias in the
estimation of the social influence effect, and demonstrate how this approach
can be implemented to estimate various social influence models with an
empirical example in R.

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