Identification and estimation of treatment effects in the presence of (correlated) neighborhood interactions: Model and Stata implementation via ntreatreg
Giovanni Cerulli
CNR-IRCrES
National Research Council of Italy
Research Institute on Sustainable Economic Growth
Rome, Italy
[email protected]
|
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
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.
View all articles by this author:
Giovanni Cerulli
View all articles with these keywords:
ntreatreg, ATEs, Rubin's causal model, SUTVA, neighborhood effects
Download citation: BibTeX RIS
Download citation and abstract: BibTeX RIS
|
