Unfaithful Probability Distributions in Binary Triple of Causality Directed Acyclic Graph
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910805903015936 |
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| author | Liu, Jingwei |
| author_facet | Liu, Jingwei |
| contents | Faithfulness is the foundation of probability distribution and graph in causal discovery and causal inference. In this paper, several unfaithful probability distribution examples are constructed in three--vertices binary causality directed acyclic graph (DAG) structure, which are not faithful to causal DAGs described in J.M.,Robins,et al. Uniform consistency in causal inference. Biometrika (2003),90(3): 491--515. And the general unfaithful probability distribution with multiple independence and conditional independence in binary triple causal DAG is given. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18337 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unfaithful Probability Distributions in Binary Triple of Causality Directed Acyclic Graph Liu, Jingwei Machine Learning Artificial Intelligence Faithfulness is the foundation of probability distribution and graph in causal discovery and causal inference. In this paper, several unfaithful probability distribution examples are constructed in three--vertices binary causality directed acyclic graph (DAG) structure, which are not faithful to causal DAGs described in J.M.,Robins,et al. Uniform consistency in causal inference. Biometrika (2003),90(3): 491--515. And the general unfaithful probability distribution with multiple independence and conditional independence in binary triple causal DAG is given. |
| title | Unfaithful Probability Distributions in Binary Triple of Causality Directed Acyclic Graph |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.18337 |