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

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  • Giovanni Cerulli

    (National Research Council of Italy)

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.

Suggested Citation

  • Giovanni Cerulli, 2017. "Identification and estimation of treatment effects in the presence of (correlated) neighborhood interactions: Model and Stata implementation via ntreatreg," Stata Journal, StataCorp LP, vol. 17(4), pages 803-833, December.
    • Handle: RePEc:tsj:stataj:v:17:y:2017:i:4:p:803-833
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    Cited by:

    1. Amadu, Festus O. & McNamara, Paul E. & Davis, Kristin E., 2021. "Soil health and grain yield impacts of climate resilient agriculture projects: Evidence from southern Malawi," Agricultural Systems, Elsevier, vol. 193(C).
    2. Amadu, Festus O. & McNamara, Paul E. & Miller, Daniel C., 2020. "Yield effects of climate-smart agriculture aid investment in southern Malawi," Food Policy, Elsevier, vol. 92(C).
    3. Spanos, Yiannis E., 2021. "Exploring heterogeneous returns to collaborative R&D: A marginal treatment effects perspective," Research Policy, Elsevier, vol. 50(5).

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