mlsanityData to 7 Oct 2026

The Recovery of Causal Poly-Trees from Statistical Data

arXiv 1304.2736 ↗First posted 27 Mar 2013cs.AIcited by 19

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Abstract (arXiv)

Poly-trees are singly connected causal networks in which variables may arise from multiple causes. This paper develops a method of recovering ply-trees from empirically measured probability distributions of pairs of variables. The method guarantees that, if the measured distributions are generated by a causal process structured as a ply-tree then the topological structure of such tree can be recovered precisely and, in addition, the causal directionality of the branches can be determined up to the maximum extent possible. The method also pinpoints the minimum (if any) external semantics required to determine the causal relationships among the variables considered.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2013.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 3; 2024: 6; 2025: 1.

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  1. 2021Efficient Bayesian network structure learning via local Markov boundary searchcited by 14
  2. 2023Causal reasoning in typical computer vision taskscited by 12
  3. 2021Structure Learning for Directed Treescited by 9
  4. 2022Learning Linear Non-Gaussian Polytree Modelscited by 7
  5. 2023Learning bounded-degree polytrees with known skeletoncited by 5
  6. 2024Bridging Weighted First Order Model Counting and Graph Polynomialscited by 5
  7. 2024Optimal estimation of Gaussian (poly)treescited by 3
  8. 2024Tensor tree learns hidden relational structures in data to construct generative modelscited by 3
  9. 2024Possible Principles for Aligned Structure Learning Agentscited by 3
  10. 2024Greedy equivalence search for nonparametric graphical modelscited by 2
  11. 2021Learning Linear Polytree Structural Equation Modelscited by 1
  12. 2021Causal Identification with Additive Noise Models: Quantifying the Effect of Noisecited by 1
7 more
  1. 2022Navigating causal deep learningcited by 1
  2. 2024Distribution Learning Meets Graph Structure Samplingcited by 1
  3. 2021The Effect of Noise Level on Causal Identification with Additive Noise Modelscited by 0
  4. 2022Estimating large causal polytrees from small samplescited by 0
  5. 2022A Comprehensively Improved Hybrid Algorithm for Learning Bayesian Networks: Multiple Compound Memory Erasingcited by 0
  6. 2023Learning Linear Gaussian Polytree Models with Interventionscited by 0
  7. 2025Optimal structure learning and conditional independence testingcited by 0