Identification and estimation of treatment effects in the presence of (correlated) neighborhood interactions: Model and Stata implementation via ntreatreg

Giovanni Cerulli

Stata Journal, 2017, vol. 17, issue 4, 803-833

Abstract: 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 accompany- ing 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 pres- ence of social interactions. In the second application, results are compared with a no-interaction setting.

Keywords: ntreatreg; ATEs; Rubin’s causal model; SUTVA; neighborhood effects (search for similar items in EconPapers)
Date: 2017
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