Estimation of causal effects in observational studies with interference between units

Mathias Lundin and Maria Karlsson ()

Statistical Methods & Applications, 2014, vol. 23, issue 3, 417-433

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

Keywords: Causal inference; Direct and indirect effects; Interference; IPW; Neighborhood effect; Observational studies; Spillover effect; SUTVA; Two-stage randomization (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10260-014-0257-8

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