Identifying Conditional Causal Effects in MPDAGs
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908459239211008 |
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| author | LaPlante, Sara Perković, Emilija |
| author_facet | LaPlante, Sara Perković, Emilija |
| contents | We consider identifying a conditional causal effect when a graph is known up to a maximally oriented partially directed acyclic graph (MPDAG). An MPDAG represents an equivalence class of graphs that is restricted by background knowledge and where all variables in the causal model are observed. We provide three results that address identification in this setting: an identification formula when the conditioning set is unaffected by treatment, a generalization of the well-known do calculus to the MPDAG setting, and an algorithm that is complete for identifying these conditional effects. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_15842 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifying Conditional Causal Effects in MPDAGs LaPlante, Sara Perković, Emilija Artificial Intelligence Methodology Machine Learning We consider identifying a conditional causal effect when a graph is known up to a maximally oriented partially directed acyclic graph (MPDAG). An MPDAG represents an equivalence class of graphs that is restricted by background knowledge and where all variables in the causal model are observed. We provide three results that address identification in this setting: an identification formula when the conditioning set is unaffected by treatment, a generalization of the well-known do calculus to the MPDAG setting, and an algorithm that is complete for identifying these conditional effects. |
| title | Identifying Conditional Causal Effects in MPDAGs |
| topic | Artificial Intelligence Methodology Machine Learning |
| url | https://arxiv.org/abs/2507.15842 |