Estimation of natural indirect effects robust to unmeasured confounding and mediator measurement error

TL;DR

This paper develops methods to estimate natural indirect effects in causal mediation analysis that remain valid despite unmeasured confounding and measurement error, under specific interaction assumptions.

Contribution

It introduces a new estimator for natural indirect effects that is robust to unmeasured confounding and measurement error, expanding applicability of mediation analysis.

Findings

Identification of indirect effects with unmeasured confounding under no interaction.

A new estimator robust to measurement error and confounding.

Simulation results demonstrating estimator performance.

Abstract

The use of causal mediation analysis to evaluate the pathways by which an exposure affects an outcome is widespread in the social and biomedical sciences. Recent advances in this area have established formal conditions for identification and estimation of natural direct and indirect effects. However, these conditions typically involve stringent no unmeasured confounding assumptions and that the mediator has been measured without error. These assumptions may fail to hold in practice where mediation methods are often applied. The goal of this paper is two-fold. First, we show that the natural indirect effect can in fact be identified in the presence of unmeasured exposure-outcome confounding provided there is no additive interaction between the mediator and unmeasured confounder(s). Second, we introduce a new estimator of the natural indirect effect that is robust to both classical measurement error of the mediator and unmeasured confounding of both exposure-outcome and mediator-outcome relations under certain no interaction assumptions. We provide formal proofs and a simulation study to demonstrate our results.