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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.
