Optimizing Causal Orderings for Generating DAGs from Data
Not on the map. Why?
Abstract (arXiv)
An algorithm for generating the structure of a directed acyclic graph from data using the notion of causal input lists is presented. The algorithm manipulates the ordering of the variables with operations which very much resemble arc reversal. Operations are only applied if the DAG after the operation represents at least the independencies represented by the DAG before the operation until no more arcs can be removed from the DAG. The resulting DAG is a minimal l-map.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Mar 2013.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 0; 2026: 1.
5 papers.
- 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 111
- 2022The Impact of Variable Ordering on Bayesian Network Structure Learningcited by 5
- 2023Order-based Structure Learning with Normalizing Flowscited by 5
- 2023Causal discovery using dynamically requested knowledgecited by 5
- 2026I-FLOP: Fast Learning of Order and Parents from Interventional Datacited by 1
- 2026I-FLOP: Fast Learning of Order and Parents from Interventional Data28 Aug 2026
- 2023Causal discovery using dynamically requested knowledge17 Oct 2023
- 2023Order-based Structure Learning with Normalizing Flows14 Aug 2023
- 2022The Impact of Variable Ordering on Bayesian Network Structure Learning17 Jun 2022
- 2022Score matching enables causal discovery of nonlinear additive noise models8 Mar 2022