Directed Cyclic Graphical Representations of Feedback Models

2013/02/20 by Peter Spirtes, Peter L. Spirtes, Spirtes, Peter L. · 7 citations
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Multi-Criteria Decision Making #Rough Sets and Fuzzy Logic #cs.AI

paper · pdf · doi:10.48550/arxiv.1302.4982

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

arxiv created 2013/02/20 · openalex publication_date 2013/02/20 · arxiv updated 2013/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Models
• An Algorithm for Fast Recovery of Sparse Causal Graphs

Cited by

• Beyond Directed Acyclic Graphs: Causal Zeros and Causal Differential Equations
• Spatially Varying Gene Regulatory Networks via Bayesian Nonparametric Covariate-Dependent Directed Cyclic Graphical Models
• Causal Discovery via Quantile Partial Effect
• Differentiable Cyclic Causal Discovery Under Unmeasured Confounders
• σ-Maximal Ancestral Graphs
• Lower Bounds on the Size of Markov Equivalence Classes
• DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms

Related

• On the Logic of Causal Models
• Identifiability Assumptions and Algorithm for Directed Graphical Models with Feedback
• Identifying Independencies in Causal Graphs with Feedback
• The Global Markov Property for a Mixture of DAGs
• On characterizing Inclusion of Bayesian Networks