Identifiable Multimodal Causal Representation Learning under Partial Latent Sharing

ArXi:2605.19135v1 Announce Type: new Causal representation learning (CRL) seeks to uncover meaningful latent variables and their corresponding causal structure from high-dimensional observational data. Although its significance, CRL identifiability remains a crucial property, as it ensures the recovery of the mechanisms behind the data generation process, and hence the interpretability and robustness of the representation. Proving identifiability in CRL is intrinsically difficult, and we address in this work an even challenging setting: multimodality.