In this article, I present a counterfactual model identifying average treatment
effects by conditional mean independence when considering peer- or
neighborhood-correlated effects, and I provide a new command,
ntreatreg, that implements such models in practical
applications. The model and its accompanying command provide an estimation of
average treatment effects when the stable unit treatment-value assumption is
relaxed under specific conditions. I present two instructional applications: the
first is a simulation exercise that shows both model implementation and
ntreatreg correctness; the second is an application
to real data, aimed at measuring the effect of housing location on crime in the
presence of social interactions. In the second application, results are compared
with a no-interaction setting.
