Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis

TL;DR

This paper introduces an orthogonalized kernel debiased machine learning method for multimodal data analysis, balancing interpretability and flexibility, with proven statistical properties and demonstrated effectiveness in neuroimaging studies.

Contribution

It proposes a novel orthogonalized kernel debiased approach for multimodal data, ensuring valid inference and improved interpretability over existing methods.

Findings

Achieves root-N consistency and asymptotic normality.

Provides valid confidence bands for primary modality effects.

Demonstrates effectiveness in neuroimaging data analysis.

Abstract

Multimodal imaging has transformed neuroscience research. While it presents unprecedented opportunities, it also imposes serious challenges. Particularly, it is difficult to combine the merits of the interpretability attributed to a simple association model with the flexibility achieved by a highly adaptive nonlinear model. In this article, we propose an orthogonalized kernel debiased machine learning approach, which is built upon the Neyman orthogonality and a form of decomposition orthogonality, for multimodal data analysis. We target the setting that naturally arises in almost all multimodal studies, where there is a primary modality of interest, plus additional auxiliary modalities. We establish the root-$N$-consistency and asymptotic normality of the estimated primary parameter, the semi-parametric estimation efficiency, and the asymptotic validity of the confidence band of the predicted primary modality effect. Our proposal enjoys, to a good extent, both model interpretability and model flexibility. It is also considerably different from the existing statistical methods for multimodal data integration, as well as the orthogonality-based methods for high-dimensional inferences. We demonstrate the efficacy of our method through both simulations and an application to a multimodal neuroimaging study of Alzheimer's disease.