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Estimation of causal effects in observational studies with interference between units

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  • Mathias Lundin
  • Maria Karlsson

Abstract

Causal effects are usually estimated under the assumption of no interference between individuals. This assumption means that the potential outcomes for one individual are unaffected by the treatments received by other individuals. In many situations, this is not reasonable to assume. Moreover, not taking interference into account could result in misleading conclusions about the effect of a treatment. For two-stage observational studies, where treatment assigment is randomized in the first stage but not in the second stage, we propose IPW estimators of direct and indirect causal effects as defined by Hudgens and Halloran (J Am Stat Assoc 103(482):832–842, 2008 ) for two-stage randomized studies. We illustrate the use of these estimators in an evaluation study of an implementation of Triple P (a parenting support program) within preschools in Uppsala, Sweden. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stmapp:v:23:y:2014:i:3:p:417-433
    DOI: 10.1007/s10260-014-0257-8
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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. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    3. James Heckman & Lance Lockner & Christopher Taber, 1999. "Human capital formation and general equilibrium treatment effects: a study of tax and tuition policy," Fiscal Studies, Institute for Fiscal Studies, vol. 20(1), pages 25-40, March.
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    5. 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.
    6. Peter M. Aronow, 2012. "A General Method for Detecting Interference Between Units in Randomized Experiments," Sociological Methods & Research, , vol. 41(1), pages 3-16, February.
    7. Bowers, Jake & Fredrickson, Mark M. & Panagopoulos, Costas, 2013. "Reasoning about Interference Between Units: A General Framework," Political Analysis, Cambridge University Press, vol. 21(1), pages 97-124, January.
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    Cited by:

    1. Denis Fougère & Nicolas Jacquemet, 2020. "Policy Evaluation Using Causal Inference Methods," SciencePo Working papers Main hal-03455978, HAL.
    2. 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.
    3. Karlsson, Maria & Lundin, Mathias, 2016. "On statistical methods for labor market evaluation under interference between units," Working Paper Series 2016:24, IFAU - Institute for Evaluation of Labour Market and Education Policy.

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