# Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis

arXiv: https://arxiv.org/abs/2103.07088 · HTML: https://mlsanity.com/paper/2103.07088/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 70 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Integrating multi-source block-wise missing data in model selection](https://mlsanity.com/paper/1901.03797/) (2019)
- [Integrative Factor Regression and Its Inference for Multimodal Data Analysis](https://mlsanity.com/paper/1911.04056/) (2019)
- [Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model](https://mlsanity.com/paper/1503.02978/) (2015)
- [A Review of Statistical Methods in Imaging Genetics](https://mlsanity.com/paper/1707.07332/) (2017)
- [Integrative Multi-View Reduced-Rank Regression: Bridging Group-Sparse and Low-Rank Models](https://mlsanity.com/paper/1807.10375/) (2018)

## Built on by (most cited first, 4 of 4)

- [Prompt-Dependent Ranking of Large Language Models with Uncertainty Quantification](https://mlsanity.com/paper/2603.03336/) (2026)
- [Online Auction Design Using Distribution-Free Uncertainty Quantification with Applications to E-Commerce](https://mlsanity.com/paper/2405.07038/) (2024)
- [Uncertainty-Aware Multimodal Learning via Conformal Shapley Intervals](https://mlsanity.com/paper/2602.00171/) (2026)
- [A Data Envelopment Analysis Approach for Assessing Fairness in Resource Allocation: Application to Kidney Exchange Programs](https://mlsanity.com/paper/2410.02799/) (2024)

## Abstract (arXiv)

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.