Discrete Nonparametric Causal Discovery Under Latent Class Confounding

arXiv:2311.07454v2 Announce Type: replace
Abstract: Directed acyclic graphs are used to model the causal structure of a system. ``Causal discovery'' describes the problem of learning this structure from data. When data is an aggregate from multiple sources (populations or environments), global confounding obscures conditional independence properties that drive many causal discovery algorithms. This setting is sometimes known as a mixture model or a latent class. While some modern methods for causal discovery are able to work around unobserved confounding in …

abstract algorithms arxiv class confounding cs.cc cs.lg data discovery drive environments global graphs math.st multiple stat.th type