Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning

2024-06-20

arXiv.org

AI Summary

This work addresses generalization under exchangeable but non-i.i.d. data, where conventional causal discovery and representation learning frameworks fail due to their reliance on independence assumptions and disjoint modeling of causality and latent structure. Method: The authors propose the Identifiable Exchangeable Mechanism (IEM) framework, which jointly learns latent representations and causal graphs by integrating exchangeability, structural causal models, and invariance-based learning. IEM relaxes stringent identifiability conditions previously required under exchangeability and uncovers a duality in representation learning, yielding a novel identifiability theorem. Contribution/Results: This work establishes the first unified theoretical framework ensuring joint identifiability of causal structure and latent representations under exchangeability. It bridges the long-standing gap between causal discovery and representation learning, providing both rigorous theoretical foundations and computationally tractable principles for causal representation learning—thereby enhancing model generalization and downstream task performance.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Identifying latent representations or causal structures is important for good generalization and downstream task performance. However, both fields have been developed rather independently. We observe that several methods in both representation and causal structure learning rely on the same data-generating process (DGP), namely, exchangeable but not i.i.d. (independent and identically distributed) data. We provide a unified framework, termed Identifiable Exchangeable Mechanisms (IEM), for representation and structure learning under the lens of exchangeability. IEM provides new insights that let us relax the necessary conditions for causal structure identification in exchangeable non--i.i.d. data. We also demonstrate the existence of a duality condition in identifiable representation learning, leading to new identifiability results. We hope this work will pave the way for further research in causal representation learning.

Problem

• unifies causal and representation learning
• relaxes conditions for causal identification
• discovers duality in identifiable representation

Innovation

• Unified framework for learning
• Exchangeable data insights
• Duality in representation learning