mlsanityData to 7 Oct 2026

Learning Directed Acyclic Graphs from Partial Orderings

arXiv 2403.16031 ↗First posted 24 Mar 2024stat.ML cs.LG stat.MEcited by 2

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Abstract (arXiv)

Directed acyclic graphs (DAGs) are commonly used to model causal relationships among random variables. In general, learning the DAG structure is both computationally and statistically challenging. Moreover, without additional information, the direction of edges may not be estimable from observational data. In contrast, given a complete causal ordering of the variables, the problem can be solved efficiently, even in high dimensions. In this paper, we consider the intermediate problem of learning DAGs when a partial causal ordering of variables is available. We propose a general estimation framework for leveraging the partial ordering and present efficient estimation algorithms for low- and high-dimensional problems. The advantages of the proposed framework are illustrated via numerical studies.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 56 references that are in the map. First posted on arXiv 24 Mar 2024.

Builds on

17 of 56 references.

References · 17

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5 more
  1. 2020Consistent Second-Order Conic Integer Programming for Learning Bayesian Networkscited by 9
  2. 2013Graph estimation with joint additive modelscited by 6
  3. 2020Directed Graphical Models and Causal Discovery for Zero-Inflated Datacited by 6
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Citing papers per year: 2026: 2.

2 papers.

  1. 2026Causal Preference Elicitationcited by 0
  2. 2026Causal Discovery Should Embrace the Wisdom of the Crowdcited by 0