AI RESEARCH
A Dialogue between Causal and Traditional Representation Learning: Toward Mutual Benefits in a Unified Formulation
arXiv CS.LG
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ArXi:2605.21058v1 Announce Type: new 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 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.