Score-Based Causal Discovery with Temporal Background Information
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866917919577866240 |
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| author | Larsen, Tobias Ellegaard Ekstrøm, Claus Thorn Petersen, Anne Helby |
| author_facet | Larsen, Tobias Ellegaard Ekstrøm, Claus Thorn Petersen, Anne Helby |
| contents | Temporal background information can improve causal discovery algorithms by orienting edges and identifying relevant adjustment sets. We develop the Temporal Greedy Equivalence Search (TGES) algorithm and terminology essential for score-based causal discovery with tiered background knowledge. TGES learns a restricted Markov equivalence class of directed acyclic graphs (DAGs) using observational data and tiered background knowledge. To construct TGES we formulate a scoring criterion that accounts for tiered background knowledge. We establish theoretical results for TGES, stating that the algorithm always returns a tiered maximally oriented partially directed acyclic graph (tiered MPDAG) and that this tiered MPDAG contains the true DAG in the large sample limit. We present a simulation study indicating a gain from using tiered background knowledge and an improved precision-recall trade-off compared to the temporal PC algorithm. We provide a real-world example on life-course health data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Score-Based Causal Discovery with Temporal Background Information Larsen, Tobias Ellegaard Ekstrøm, Claus Thorn Petersen, Anne Helby Methodology Temporal background information can improve causal discovery algorithms by orienting edges and identifying relevant adjustment sets. We develop the Temporal Greedy Equivalence Search (TGES) algorithm and terminology essential for score-based causal discovery with tiered background knowledge. TGES learns a restricted Markov equivalence class of directed acyclic graphs (DAGs) using observational data and tiered background knowledge. To construct TGES we formulate a scoring criterion that accounts for tiered background knowledge. We establish theoretical results for TGES, stating that the algorithm always returns a tiered maximally oriented partially directed acyclic graph (tiered MPDAG) and that this tiered MPDAG contains the true DAG in the large sample limit. We present a simulation study indicating a gain from using tiered background knowledge and an improved precision-recall trade-off compared to the temporal PC algorithm. We provide a real-world example on life-course health data. |
| title | Score-Based Causal Discovery with Temporal Background Information |
| topic | Methodology |
| url | https://arxiv.org/abs/2502.06232 |