Interference and Sensitivity Analysis

TL;DR

This paper reviews causal inference with interference, discusses identification challenges, and develops sensitivity analysis methods for infectiousness effects and unmeasured confounding in such settings.

Contribution

It introduces new sensitivity analysis techniques for causal effects under interference, especially when effects are not identifiable due to unmeasured confounders or lack of randomization.

Findings

Developed sensitivity analysis methods for infectiousness effects in vaccine trials.

Created techniques for unmeasured confounding under interference, generalizing existing methods.

Compared and contrasted two new sensitivity analysis approaches.

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 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.