title: Interference and Sensitivity Analysis.
abstract: 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 in the presence of unmeasured confounding which generalize analogous techniques when interference is absent. These two techniques for unmeasured confounding are compared and contrasted.
authors.affiliation: Departments of Epidemiology and Biostatistics, Harvard School of Public Health, University of Washington.
authors.forename: Tyler J
authors.initials: TJ
authors.lastname: VanderWeele
authors.affiliation: Departments of Epidemiology and Biostatistics, Harvard School of Public Health, University of Washington.
authors.forename: Eric J
authors.initials: EJ
authors.lastname: Tchetgen Tchetgen
authors.affiliation: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center and Department of Biostatistics, University of Washington.
authors.forename: M Elizabeth
authors.initials: ME
authors.lastname: Halloran
keywords: Causal inference; infectiousness effect; interference; sensitivity analysis; spillover effect; stable unit treatment value assumption; vaccine trial