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A Machine Learning Based Approach Towards High-dimensional Mediation Analysis by Tanmay Nath; Brian Caffo; Tor Wager; Martin Lindquist is a scholarly article available to read on EtoBox.

What is A Machine Learning Based Approach Towards High-dimensional Mediation Analysis about?

Mediation analysis is used to investigate the role of intermediate variables (mediators) that lie in the path between an exposure and an outcome variable. While significant research has focused on developing methods for assessing the influence of mediators on the exposure-outcome relationship, current approaches do not easily extend to settings where the mediator is high-dimensional. These situations are becoming increasingly common with the rapid increase of new applications measuring massive numbers of variables, including brain imaging, genomics, and metabolomics. In this work, we introduce a novel machine learning based method for identifying high dimensional mediators. The proposed algorithm iterates between using a machine learning model to map the high-dimensional mediators onto a lower-dimensional space, and using the predicted values as input in a standard three-variable mediation model. Hence, the machine learning model is trained to maximize the likelihood of the mediation model. Importantly, the proposed algorithm is agnostic to the machine learning model that is used, providing significant flexibility in the types of situations where it can be used. We illustrate the p

Author

Tanmay Nath; Brian Caffo; Tor Wager; Martin Lindquist

Published

2022

Language

EN