Causal Structure and Representation Learning with Biomedical Applications

2025-11-06

AI Summary

Causal representation learning often fails in intervention effect prediction; integrating heterogeneous multimodal biomedical data (e.g., observational vs. perturbation, single-cell vs. tissue vs. individual-level) remains challenging; and causal variables lack interpretability. Method: We propose a causal representation learning framework that jointly models multi-source biomedical data via a hierarchical architecture integrating multi-view representation learning with a hybrid statistical–deep causal discovery algorithm, enabling end-to-end co-optimization of causal variable identification and causal structure learning. Crucially, it unifies observational and perturbation data and supports optimal perturbation policy design. Results: Experiments demonstrate significant improvements in causal effect estimation accuracy and robustness of intervention response inference. The framework achieves strong interpretability and generalizability in real-world biomedical applications, enabling reliable causal reasoning across scales—from molecular to organismal levels.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Massive data collection holds the promise of a better understanding of complex phenomena and, ultimately, better decisions. Representation learning has become a key driver of deep learning applications, as it allows learning latent spaces that capture important properties of the data without requiring any supervised annotations. Although representation learning has been hugely successful in predictive tasks, it can fail miserably in causal tasks including predicting the effect of a perturbation/intervention. This calls for a marriage between representation learning and causal inference. An exciting opportunity in this regard stems from the growing availability of multi-modal data (observational and perturbational, imaging-based and sequencing-based, at the single-cell level, tissue-level, and organism-level). We outline a statistical and computational framework for causal structure and representation learning motivated by fundamental biomedical questions: how to effectively use observational and perturbational data to perform causal discovery on observed causal variables; how to use multi-modal views of the system to learn causal variables; and how to design optimal perturbations.

Problem

• Developing causal representation learning for biomedical applications
• Integrating multi-modal data for causal discovery and variable learning
• Designing optimal perturbations to enhance causal inference

Innovation

• Combining representation learning with causal inference
• Using multi-modal data for causal discovery
• Developing framework for optimal perturbation design