Towards Robust Causal Effect Identification Beyond Markov Equivalence
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866915350180790272 |
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| author | Teh, Kai Z. Sadeghi, Kayvan Soo, Terry |
| author_facet | Teh, Kai Z. Sadeghi, Kayvan Soo, Terry |
| contents | Causal effect identification typically requires a fully specified causal graph, which can be difficult to obtain in practice. We provide a sufficient criterion for identifying causal effects from a candidate set of Markov equivalence classes with added background knowledge, which represents cases where determining the causal graph up to a single Markov equivalence class is challenging. Such cases can happen, for example, when the untestable assumptions (e.g. faithfulness) that underlie causal discovery algorithms do not hold. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Robust Causal Effect Identification Beyond Markov Equivalence Teh, Kai Z. Sadeghi, Kayvan Soo, Terry Methodology Causal effect identification typically requires a fully specified causal graph, which can be difficult to obtain in practice. We provide a sufficient criterion for identifying causal effects from a candidate set of Markov equivalence classes with added background knowledge, which represents cases where determining the causal graph up to a single Markov equivalence class is challenging. Such cases can happen, for example, when the untestable assumptions (e.g. faithfulness) that underlie causal discovery algorithms do not hold. |
| title | Towards Robust Causal Effect Identification Beyond Markov Equivalence |
| topic | Methodology |
| url | https://arxiv.org/abs/2506.15561 |