Causal Mediation Analysis with a Three-Dimensional Image Mediator

TL;DR

This paper introduces a novel causal mediation analysis method using three-dimensional brain images as mediators, addressing high-dimensional data challenges in neuroscience and related fields.

Contribution

It develops a new approach for causal mediation analysis with 3D image mediators, including identification conditions, estimation techniques, and inference methods.

Findings

White matter in frontal-temporal regions mediates effects on IQ.

Method validated through simulations under various scenarios.

Applied to study on delivery mode and child's IQ.

Abstract

Causal mediation analysis is increasingly abundant in biology, psychology, and epidemiology studies, etc. In particular, with the advent of the big data era, the issue of high-dimensional mediators is becoming more prevalent. In neuroscience, with the widespread application of magnetic resonance technology in the field of brain imaging, studies on image being a mediator emerged. In this study, a novel causal mediation analysis method with a three-dimensional image mediator is proposed. We define the average casual effects under the potential outcome framework, explore several sufficient conditions for the valid identification, and develop techniques for estimation and inference. To verify the effectiveness of the proposed method, a series of simulations under various scenarios is performed. Finally, the proposed method is applied to a study on the causal effect of mother$^{\prime}$s delivery mode on child$^{\prime}$s IQ development. It is found that the white matter in certain regions of the frontal-temporal areas has mediating effects.