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Learning Linear Gaussian Polytree Models with Interventions

arXiv 2311.04636 ↗First posted 8 Nov 2023stat.ML cs.LGcited by 0

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Abstract (arXiv)

We present a consistent and highly scalable local approach to learn the causal structure of a linear Gaussian polytree using data from interventional experiments with known intervention targets. Our methods first learn the skeleton of the polytree and then orient its edges. The output is a CPDAG representing the interventional equivalence class of the polytree of the true underlying distribution. The skeleton and orientation recovery procedures we use rely on second order statistics and low-dimensional marginal distributions. We assess the performance of our methods under different scenarios in synthetic data sets and apply our algorithm to learn a polytree in a gene expression interventional data set. Our simulation studies demonstrate that our approach is fast, has good accuracy in terms of structural Hamming distance, and handles problems with thousands of nodes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 61 references that are in the map. First posted on arXiv 8 Nov 2023.

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References · 21

  1. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  2. 2017Invariant Causal Prediction for Nonlinear Modelscited by 140
  3. 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
  4. 2018Characterizing and Learning Equivalence Classes of Causal DAGs under Interventionscited by 76
  5. 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
  6. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  7. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  8. 2017Learning Causal Structures Using Regression Invariancecited by 32
  9. 2021Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Biascited by 29
  10. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  11. 2013Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphscited by 25
  12. 2020A Recursive Markov Boundary-Based Approach to Causal Structure Learningcited by 21
9 more
  1. 2013The Recovery of Causal Poly-Trees from Statistical Datacited by 19
  2. 2013Learning Polytreescited by 14
  3. 2021Learning Bayesian Networks in the Presence of Structural Side Informationcited by 12
  4. 2022Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variablescited by 10
  5. 2021Structure Learning for Directed Treescited by 9
  6. 2022Learning Linear Non-Gaussian Polytree Modelscited by 7
  7. 2021On the Parameterized Complexity of Polytree Learningcited by 6
  8. 2021A Fast Non-parametric Approach for Local Causal Structure Learningcited by 5
  9. 2018The Hellinger Correlationcited by 4

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