Graphical models for mediation analysis

TL;DR

This paper reviews methods for identifying path-specific effects in mediation analysis using graphical models, especially under complex structural equation models with potential unmeasured variables.

Contribution

It provides a comprehensive review of identification strategies for path-specific effects in nonparametric structural equation models, including cases with unmeasured variables.

Findings

Identifies which path-specific effects can be determined under certain models.

Shows how to identify these effects using graphical models.

Clarifies the limitations of adjustment for confounding in mediation analysis.

Abstract

Mediation analysis seeks to infer how much of the effect of an exposure on an outcome can be attributed to specific pathways via intermediate variables or mediators. This requires identification of so-called path-specific effects. These express how a change in exposure affects those intermediate variables (along certain pathways), and how the resulting changes in those variables in turn affect the outcome (along subsequent pathways). However, unlike identification of total effects, adjustment for confounding is insufficient for identification of path-specific effects because their magnitude is also determined by the extent to which individuals who experience large exposure effects on the mediator, tend to experience relatively small or large mediator effects on the outcome. This chapter therefore provides an accessible review of identification strategies under general nonparametric structural equation models (with possibly unmeasured variables), which rule out certain such dependencies. In particular, it is shown which path-specific effects can be identified under such models, and how this can be done.