Identifying Independencies in Causal Graphs with Feedback
Not on the map. Why?
Abstract (arXiv)
We show that the d -separation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2013.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 2; 2025: 0; 2026: 1.
7 papers.
- 2021Dynamic Causal Bayesian Optimizationcited by 17
- 2023No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imagingcited by 11
- 2023Establishing Markov Equivalence in Cyclic Directed Graphscited by 5
- 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
- 2023On the Foundations of Cycles in Bayesian Networkscited by 2
- 2024Characterising Interventions in Causal Gamescited by 0
- 2024Robust Causal Analysis of Linear Cyclic Systems With Hidden Confounderscited by 0
- 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning5 Mar 2026
- 2024Robust Causal Analysis of Linear Cyclic Systems With Hidden Confounders18 Nov 2024
- 2024Characterising Interventions in Causal Games13 Jun 2024
- 2023Establishing Markov Equivalence in Cyclic Directed Graphs1 Sep 2023
- 2023No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging31 Jul 2023
- 2023On the Foundations of Cycles in Bayesian Networks20 Jan 2023
- 2021Dynamic Causal Bayesian Optimization26 Oct 2021