Identifying Independencies in Causal Graphs with Feedback

Abstract

We show that the d -separation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables.

Appears in Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI1996)

References in corpus (1)

• On the Testability of Causal Models with Latent and Instrumental Variables

Cited by in corpus (4)

• Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)
• On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note)
• Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
• Asymmetric separation for local independence graphs