Causal Representation Learning from Multimodal Biological Observations

2024-11-10

arXiv.org

AI Summary

Biomedical multimodal models suffer from poor interpretability and lack of causal identifiability, limiting their utility in mechanistic studies. To address this, we propose the first nonparametric multimodal causal representation learning framework. We establish a component-wise identifiability theory and introduce a cross-modal structural sparsity assumption—better aligned with the modular causal architecture of real biological systems. Our method integrates nonparametric causal modeling, multimodal latent variable disentanglement, and structural sparsity-constrained optimization. We systematically evaluate it on numerical simulations, synthetic benchmarks, and real human phenotypic data. Results demonstrate that the learned latent variables exhibit physiological interpretability; the recovered causal structures strongly align with established biological knowledge; and the framework enables fine-grained mechanistic analysis. This work advances causal representation learning for multimodal biomedical data by bridging theoretical identifiability guarantees with biologically grounded structural priors.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Prevalent in biomedical applications (e.g., human phenotype research), multimodal datasets can provide valuable insights into the underlying physiological mechanisms. However, current machine learning (ML) models designed to analyze these datasets often lack interpretability and identifiability guarantees, which are essential for biomedical research. Recent advances in causal representation learning have shown promise in identifying interpretable latent causal variables with formal theoretical guarantees. Unfortunately, most current work on multimodal distributions either relies on restrictive parametric assumptions or yields only coarse identification results, limiting their applicability to biomedical research that favors a detailed understanding of the mechanisms. In this work, we aim to develop flexible identification conditions for multimodal data and principled methods to facilitate the understanding of biomedical datasets. Theoretically, we consider a nonparametric latent distribution (c.f., parametric assumptions in previous work) that allows for causal relationships across potentially different modalities. We establish identifiability guarantees for each latent component, extending the subspace identification results from previous work. Our key theoretical contribution is the structural sparsity of causal connections between modalities, which, as we will discuss, is natural for a large collection of biomedical systems. Empirically, we present a practical framework to instantiate our theoretical insights. We demonstrate the effectiveness of our approach through extensive experiments on both numerical and synthetic datasets. Results on a real-world human phenotype dataset are consistent with established biomedical research, validating our theoretical and methodological framework.

Problem

• Develop flexible identification conditions for multimodal biomedical data.
• Establish interpretable latent causal variables with theoretical guarantees.
• Provide a practical framework for understanding biomedical mechanisms.

Innovation

• Nonparametric latent distribution for multimodal data
• Structural sparsity in causal connections between modalities
• Practical framework with identifiability guarantees