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November 2014 Interference and Sensitivity Analysis
Tyler J. VanderWeele, Eric J. Tchetgen Tchetgen, M. Elizabeth Halloran
Statist. Sci. 29(4): 687-706 (November 2014). DOI: 10.1214/14-STS479


Causal inference with interference is a rapidly growing area. The literature has begun to relax the “no-interference” assumption that the treatment received by one individual does not affect the outcomes of other individuals. In this paper we briefly review the literature on causal inference in the presence of interference when treatments have been randomized. We then consider settings in which causal effects in the presence of interference are not identified, either because randomization alone does not suffice for identification or because treatment is not randomized and there may be unmeasured confounders of the treatment–outcome relationship. We develop sensitivity analysis techniques for these settings. We describe several sensitivity analysis techniques for the infectiousness effect which, in a vaccine trial, captures the effect of the vaccine of one person on protecting a second person from infection even if the first is infected. We also develop two sensitivity analysis techniques for causal effects under interference in the presence of unmeasured confounding which generalize analogous techniques when interference is absent. These two techniques for unmeasured confounding are compared and contrasted.


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Tyler J. VanderWeele. Eric J. Tchetgen Tchetgen. M. Elizabeth Halloran. "Interference and Sensitivity Analysis." Statist. Sci. 29 (4) 687 - 706, November 2014.


Published: November 2014
First available in Project Euclid: 15 January 2015

zbMATH: 1331.62443
MathSciNet: MR3300366
Digital Object Identifier: 10.1214/14-STS479

Rights: Copyright © 2014 Institute of Mathematical Statistics


Vol.29 • No. 4 • November 2014
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