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ntreatreg: a Stata module for estimation of treatment effects in the presence of neighborhood interactions

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  • Giovanni Cerulli

    (Institute for Economic Research on Firms and Growth, Rome)

Abstract

This paper presents a parametric counter-factual model identifying average treatment effects (ATEs) by conditional mean independence when externality (or neighborhood) effects are incorporated within the traditional Rubin-potential outcome model. As such, it tries to generalize the usual control-function regression, widely used in program evaluation and epidemiology, when the stable unit treatment value assumption (SUTVA) is relaxed. As a by-product, the paper also presents ntreatreg, a user-written Stata routine for estimating ATEs when social interaction may be present. Finally, an instructional application of the model and of its Stata implementation (using ntreatreg) through two examples (the first on the effect of housing location on crime; the second on the effect of education on fertility) is shown and results compared with a no-interaction setting.

Suggested Citation

  • Giovanni Cerulli, 2014. "ntreatreg: a Stata module for estimation of treatment effects in the presence of neighborhood interactions," United Kingdom Stata Users' Group Meetings 2014 15, Stata Users Group.
    • Handle: RePEc:boc:usug14:15
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    References listed on IDEAS

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    1. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
    2. Sobel, Michael E., 2006. "What Do Randomized Studies of Housing Mobility Demonstrate?: Causal Inference in the Face of Interference," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1398-1407, December.
    3. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    4. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    5. Hudgens, Michael G. & Halloran, M. Elizabeth, 2008. "Toward Causal Inference With Interference," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 832-842, June.
    6. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    7. Rosenbaum, Paul R., 2007. "Interference Between Units in Randomized Experiments," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 191-200, March.
    8. Wooldridge, Jeffrey M., 1997. "On two stage least squares estimation of the average treatment effect in a random coefficient model," Economics Letters, Elsevier, vol. 56(2), pages 129-133, October.
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