A Sparsity Principle for Partially Observable Causal Representation Learning

arXiv:2403.08335v1 Announce Type: new
Abstract: Causal representation learning aims at identifying high-level causal variables from perceptual data. Most methods assume that all latent causal variables are captured in the high-dimensional observations. We instead consider a partially observed setting, in which each measurement only provides information about a subset of the underlying causal state. Prior work has studied this setting with multiple domains or views, each depending on a fixed subset of latents. Here, we focus on learning from unpaired observations …

abstract arxiv causal cs.ai cs.lg data information measurement observable representation representation learning sparsity stat.ml type variables