Directed Cyclic Graphical Representations of Feedback Models

Abstract

The use of directed acyclic graphs (DAGs) to represent conditional independence relations among random variables has proved fruitful in a variety of ways. Recursive structural equation models are one kind of DAG model. However, non-recursive structural equation models of the kinds used to model economic processes are naturally represented by directed cyclic graphs with independent errors, a characterization of conditional independence errors, a characterization of conditional independence constraints is obtained, and it is shown that the result generalizes in a natural way to systems in which the error variables or noises are statistically dependent. For non-linear systems with independent errors a sufficient condition for conditional independence of variables in associated distributions is obtained.

Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)

References in corpus (1)

• A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Models

Cited by in corpus (5)

• An Alternative Markov Property for Chain Graphs
• On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note)
• A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Models
• Modeling Discrete Interventional Data using Directed Cyclic Graphical Models
• Asymmetric separation for local independence graphs