2014/11/01 by Tyler J. VanderWeele, Eric J. Tchetgen Tchetgen, M. Elizabeth Halloran · 1 citation
Computer Science · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Bayesian Modeling and Causal Inference #Qualitative Comparative Analysis Research #stat.ME
paper · pdf · doi:10.1214/14-sts479
published as Statistical Science 2014, Vol. 29, No. 4, 687-706 · Published in at http://dx.doi.org/10.1214/14-STS479 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2014/11/01 · arxiv created 2015/03/05 · arxiv updated 2015/03/06 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/01
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.