Orthogonalized Kernel Debiased Machine Learning for Multimodal 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,

SUBMITTER: Dai X 

PROVIDER: S-EPMC10530774 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature