On the Use of Instrumental Variables in Mediation Analysis

TL;DR

This paper explores how instrumental variables can be used in causal mediation analysis to identify mechanisms of treatment effects, especially when unobserved confounders are present, highlighting limitations and assumptions involved.

Contribution

It provides a theoretical framework for using IV methods in mediation analysis with binary treatment and IV, clarifying assumptions needed and limitations of IV estimands.

Findings

IV methods accommodate unobserved confounders in mediation analysis

Additional monotonicity assumptions are necessary for identification

IV estimands may not always match target causal parameters

Abstract

Empirical researchers are often interested in not only whether a treatment affects an outcome of interest, but also how the treatment effect arises. Causal mediation analysis provides a formal framework to identify causal mechanisms through which a treatment affects an outcome. The most popular identification strategy relies on so-called sequential ignorability (SI) assumption which requires that there is no unobserved confounder that lies in the causal paths between the treatment and the outcome. Despite its popularity, such assumption is deemed to be too strong in many settings as it excludes the existence of unobserved confounders. This limitation has inspired recent literature to consider an alternative identification strategy based on an instrumental variable (IV). This paper discusses the identification of causal mediation effects in a setting with a binary treatment and a binary instrumental variable that is both assumed to be random. We show that while IV methods allow for the possible existence of unobserved confounders, additional monotonicity assumptions are required unless the strong constant effect is assumed. Furthermore, even when such monotonicity assumptions are satisfied, IV estimands are not necessarily equivalent to target parameters.