Generalized Criterion for Identifiability of Additive Noise Models Using Majorization

arXiv:2404.05148v1 Announce Type: cross
Abstract: The discovery of causal relationships from observational data is very challenging. Many recent approaches rely on complexity or uncertainty concepts to impose constraints on probability distributions, aiming to identify specific classes of directed acyclic graph (DAG) models. In this paper, we introduce a novel identifiability criterion for DAGs that places constraints on the conditional variances of additive noise models. We demonstrate that this criterion extends and generalizes existing identifiability criteria in the literature that employ …

abstract arxiv causal complexity concepts constraints criterion dag data discovery generalized graph identify noise novel paper probability relationships stat.me stat.ml type uncertainty