Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing

arXiv:2604.13218v1 Announce Type: cross Abstract: Causal representation learning (CRL) aims to identify the underlying latent variables from high-dimensional observations, even when variables are dependent with each other. We study this problem for latent variables that follow a potentially degenerate Gaussian mixture distribution and that are only observed through the transformation via a piecewise affine mixing function. We provide a series of progressively stronger identifiability results for