# Identifying Independencies in Causal Graphs with Feedback

arXiv: https://arxiv.org/abs/1302.3595 · HTML: https://mlsanity.com/paper/1302.3595/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2013.

## Built on by (most cited first, 7 of 7)

- [Dynamic Causal Bayesian Optimization](https://mlsanity.com/paper/2110.13891/) (2021)
- [No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging](https://mlsanity.com/paper/2307.16526/) (2023)
- [Establishing Markov Equivalence in Cyclic Directed Graphs](https://mlsanity.com/paper/2309.03092/) (2023)
- [Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning](https://mlsanity.com/paper/2603.04780/) (2026)
- [On the Foundations of Cycles in Bayesian Networks](https://mlsanity.com/paper/2301.08608/) (2023)
- [Characterising Interventions in Causal Games](https://mlsanity.com/paper/2406.09318/) (2024)
- [Robust Causal Analysis of Linear Cyclic Systems With Hidden Confounders](https://mlsanity.com/paper/2411.11590/) (2024)

## 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.