A Dialogue between Causal and Traditional Representation Learning: Toward Mutual Benefits in a Unified Formulation

2026-05-20

AI Summary

Causal representation learning has long remained disconnected from traditional representation learning, resulting in inconsistent terminology, problem formulations, and evaluation criteria that hinder their synergistic development. This work proposes the first unified formal framework that decouples representation learning into a task component—specifying the information to preserve—and a constraint component—defining the structure of the latent space—thereby integrating causal identification theory with empirically driven objective functions. The framework elucidates a bidirectional benefit between causal and traditional approaches: task design informs effective structural constraints, while appropriate constraints enhance task performance. Evaluated on the CausalVerse benchmark, the study demonstrates that the efficacy of causal constraints is highly contingent on alignment with the downstream task, and that under this unified paradigm, both model performance and structural interpretability are significantly improved.

Abstract

Causal representation learning (CRL) and traditional representation learning have largely developed along different trajectories. Traditional representation learning has been driven mainly by applications and empirical objectives, whereas CRL has focused more on theoretical questions, particularly identifiability. This difference in emphasis has created a gap between the two fields in terminology, problem formulation, and evaluation, limiting communication and sometimes leading to disconnected or redundant efforts. In this paper, we argue that these two fields should be brought into dialogue rather than treated as separate paradigms. To this end, we introduce a unified formulation in which the representation learning is characterized by two components: a task component, which specifies what information the learned representation is required to preserve, and a constraint component, which specifies what structure is imposed on the latent space. Under this formulation, the benefits run in both directions. CRL provides theoretical tools for understanding when structured latent constraints are useful or necessary, while traditional representation learning offers practical insights on task design and objective choice that can improve the development of CRL methods. To illustrate this interaction, we experimentally study how different task components affect the behavior of CRL methods under different structured constraints. Results on CausalVerse show that the effectiveness of causal constraints depends strongly on the tasks with which they are paired.

Problem

• causal representation learning
• traditional representation learning
• unified formulation
• identifiability
• latent constraints

Innovation

• causal representation learning
• unified formulation
• task-constraint framework
• structured latent space
• mutual benefits