Causal Inference and Sensitivity Analysis with Interference

• Activity Number: 401
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): M. Elizabeth Halloran*
• Companies: ASA
• Keywords: causal inference ; interference ; sensitivity analysis ; randomization ; confounders

Abstract

Here we consider causal inference in the presence of interference, that is when treatment received by one individual may affect the outcomes of other individuals. We define several causal estimands for different effects of interventions in populations when interference is present. 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. Among others, we 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. (Parts of this paper will appear in Statistical Science, special issue on causality.)