IDEAS home Printed from https://ideas.repec.org/p/boc/isug14/06.html
   My bibliography  Save this paper

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

Author

Listed:
  • Giovanni Cerulli

    (CNR-CERIS National Research Council of Italy, Institute for Economic Research on Firms and Growth)

Abstract

This presentation presents a parametric counterfactual 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 command 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," Italian Stata Users' Group Meetings 2014 06, Stata Users Group.
    • Handle: RePEc:boc:isug14:06
    as

    Download full text from publisher

    File URL: http://www.stata.com/meeting/italy14/abstracts/materials/it14_cerulli_te_nbhd.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    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, September.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giovanni Cerulli, 2014. "Identification and Estimation of Treatment Effects in the Presence of Neighbourhood Interactions," CERIS Working Paper 201404, Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY -NOW- Research Institute on Sustainable Economic Growth - Moncalieri (TO) ITALY.
    2. Giovanni Cerulli & Roberto Gabriele & Enrico Tundis, 2014. "Evaluating locally-based policies in the presence of neighbourhood effects: The case of touristic accommodation in the Garda district of Trentino," ERSA conference papers ersa14p715, European Regional Science Association.
    3. Sourafel Girma & Yundan Gong & Holger Görg & Sandra Lancheros, 2016. "Estimating direct and indirect effects of foreign direct investment on firm productivity in the presence of interactions between firms," World Scientific Book Chapters, in: MULTINATIONAL ENTERPRISES AND HOST COUNTRY DEVELOPMENT, chapter 12, pages 227-239, World Scientific Publishing Co. Pte. Ltd..
    4. Kosuke Imai & Zhichao Jiang, 2020. "Identification and sensitivity analysis of contagion effects in randomized placebo‐controlled trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1637-1657, October.
    5. Clarke, Damian, 2017. "Estimating Difference-in-Differences in the Presence of Spillovers," MPRA Paper 81604, University Library of Munich, Germany.
    6. C. Tort`u & I. Crimaldi & F. Mealli & L. Forastiere, 2020. "Modelling Network Interference with Multi-valued Treatments: the Causal Effect of Immigration Policy on Crime Rates," Papers 2003.10525, arXiv.org, revised Jun 2020.
    7. Dionissi Aliprantis, 2011. "Assessing the evidence on neighborhood effects from moving to opportunity," Working Papers (Old Series) 1101, Federal Reserve Bank of Cleveland.
    8. Laura Forastiere & Patrizia Lattarulo & Marco Mariani & Fabrizia Mealli & Laura Razzolini, 2019. "Exploring encouragement, treatment and spillover effects using principal stratification, with application to a field experiment on teens' museum attendance," Natural Field Experiments 00673, The Field Experiments Website.
    9. Davide Viviano, 2020. "Experimental Design under Network Interference," Papers 2003.08421, arXiv.org, revised Jun 2020.
    10. Chiba, Yasutaka, 2012. "A note on bounds for the causal infectiousness effect in vaccine trials," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1422-1429.
    11. Mathias Lundin & Maria Karlsson, 2014. "Estimation of causal effects in observational studies with interference between units," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 417-433, August.
    12. Arpino, Bruno & Mattei, Alessandra, 2013. "Assessing the Impact of Financial Aids to Firms: Causal Inference in the presence of Interference," MPRA Paper 51795, University Library of Munich, Germany.
    13. Laura Forastiere & Fabrizia Mealli & Tyler J. VanderWeele, 2016. "Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets Using Bayesian Principal Stratification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 510-525, April.
    14. Francis J. DiTraglia & Camilo Garcia-Jimeno & Rossa O'Keeffe-O'Donovan & Alejandro Sanchez-Becerra, 2020. "Identifying Causal Effects in Experiments with Social Interactions and Non-compliance," Papers 2011.07051, arXiv.org.
    15. Guillaume W Basse & Edoardo M Airoldi, 2018. "Model-assisted design of experiments in the presence of network-correlated outcomes," Biometrika, Biometrika Trust, vol. 105(4), pages 849-858.
    16. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
    17. Alan Andre Borges da Costa & Sergio Pinheiro Firpo, 2018. "An analysis of the distributive effects of public policies and their spillovers," Working Papers, Department of Economics 2018_06, University of São Paulo (FEA-USP).
    18. Christopher Baum & Giovanni Cerulli, CNR-IRCrES, 2016. "Estimating a dose-response function with heterogeneous response to confounders when treatment is continuous and endogenous," EcoMod2016 9388, EcoMod.
    19. Rigdon, Joseph & Hudgens, Michael G., 2015. "Exact confidence intervals in the presence of interference," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 130-135.
    20. David Choi, 2017. "Estimation of Monotone Treatment Effects in Network Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1147-1155, July.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boc:isug14:06. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F Baum). General contact details of provider: http://edirc.repec.org/data/stataea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.