Learning DAGs without imposing acyclicity
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Abstract (arXiv)
We explore if it is possible to learn a directed acyclic graph (DAG) from data without imposing explicitly the acyclicity constraint. In particular, for Gaussian distributions, we frame structural learning as a sparse matrix factorization problem and we empirically show that solving an ℓ1-penalized optimization yields to good recovery of the true graph and, in general, to almost-DAG graphs. Moreover, this approach is computationally efficient and is not affected by the explosion of combinatorial complexity as in classical structural learning algorithms.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jun 2020.
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Citing papers per year: 2021: 5; 2022: 2; 2023: 2; 2024: 1.
10 papers.
- 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
- 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
- 2021DAGs with No Curl: An Efficient DAG Structure Learning Approachcited by 53
- 2021Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Datacited by 22
- 2023Boosting Differentiable Causal Discovery via Adaptive Sample Reweightingcited by 5
- 2023Discovering Dynamic Causal Space for DAG Structure Learningcited by 4
- 2022Differentiable Invariant Causal Discoverycited by 3
- 2021Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristicscited by 2
- 2022Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decompositioncited by 1
- 2024Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networkscited by 0
- 2024Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networks15 Aug 2024
- 2023Discovering Dynamic Causal Space for DAG Structure Learning5 Jun 2023
- 2023Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting6 Mar 2023
- 2022Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition25 Nov 2022
- 2022Differentiable Invariant Causal Discovery31 May 2022
- 2021Efficient Neural Causal Discovery without Acyclicity Constraints22 Jul 2021
- 2021Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristics1 Jul 2021
- 2021DAGs with No Curl: An Efficient DAG Structure Learning Approach14 Jun 2021
- 2021Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data16 Apr 2021
- 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discovery3 Mar 2021