Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables

arXiv:2403.06499v1 Announce Type: cross
Abstract: Causal discovery in the presence of unobserved common causes from observational data only is a crucial but challenging problem. We categorize all possible causal relationships between two random variables into the following four categories and aim to identify one from observed data: two cases in which either of the direct causality exists, a case that variables are independent, and a case that variables are confounded by latent confounders. Although existing methods have been proposed to …

abstract aim arxiv causal code continuous cs.it cs.lg data detection discovery identify math.it mixed random relationships stat.ml type variables