Assumptions of IV Methods for Observational Epidemiology

TL;DR

This paper reviews various instrumental variable methods in observational epidemiology, highlighting their assumptions, differences, and biases, and emphasizes the importance of sensitivity analysis due to potential violations like effect modification.

Contribution

It provides a detailed comparison of common IV approaches, discusses their assumptions, and investigates bias under assumption violations through simulations.

Findings

All IV methods face bias issues with effect modification by unobserved confounders.

Different IV methods target different causal parameters and have varying assumptions.

Sensitivity analysis is recommended for practical IV applications.

Abstract

Instrumental variable (IV) methods are becoming increasingly popular as they seem to offer the only viable way to overcome the problem of unobserved confounding in observational studies. However, some attention has to be paid to the details, as not all such methods target the same causal parameters and some rely on more restrictive parametric assumptions than others. We therefore discuss and contrast the most common IV approaches with relevance to typical applications in observational epidemiology. Further, we illustrate and compare the asymptotic bias of these IV estimators when underlying assumptions are violated in a numerical study. One of our conclusions is that all IV methods encounter problems in the presence of effect modification by unobserved confounders. Since this can never be ruled out for sure, we recommend that practical applications of IV estimators be accompanied routinely by a sensitivity analysis.