A Conditional Independence Test in the Presence of Discretization

arXiv:2404.17644v1 Announce Type: cross
Abstract: Testing conditional independence has many applications, such as in Bayesian network learning and causal discovery. Different test methods have been proposed. However, existing methods generally can not work when only discretized observations are available. Specifically, consider $X_1$, $\tilde{X}_2$ and $X_3$ are observed variables, where $\tilde{X}_2$ is a discretization of latent variables $X_2$. Applying existing test methods to the observations of $X_1$, $\tilde{X}_2$ and $X_3$ can lead to a false conclusion about the underlying conditional independence …

abstract applications arxiv bayesian causal cs.ai cs.lg discovery however network stat.ml test testing type variables work