One-Shot Multi-Label Causal Discovery in High-Dimensional Event Sequences
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914155501453312 |
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| author | Math, Hugo Schön, Robin Lienhart, Rainer |
| author_facet | Math, Hugo Schön, Robin Lienhart, Rainer |
| contents | Understanding causality in event sequences with thousands of sparse event types is critical in domains such as healthcare, cybersecurity, or vehicle diagnostics, yet current methods fail to scale. We present OSCAR, a one-shot causal autoregressive method that infers per-sequence Markov Boundaries using two pretrained Transformers as density estimators. This enables efficient, parallel causal discovery without costly global CI testing. On a real-world automotive dataset with 29,100 events and 474 labels, OSCAR recovers interpretable causal structures in minutes, while classical methods fail to scale, enabling practical scientific diagnostics at production scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23213 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | One-Shot Multi-Label Causal Discovery in High-Dimensional Event Sequences Math, Hugo Schön, Robin Lienhart, Rainer Machine Learning Artificial Intelligence Understanding causality in event sequences with thousands of sparse event types is critical in domains such as healthcare, cybersecurity, or vehicle diagnostics, yet current methods fail to scale. We present OSCAR, a one-shot causal autoregressive method that infers per-sequence Markov Boundaries using two pretrained Transformers as density estimators. This enables efficient, parallel causal discovery without costly global CI testing. On a real-world automotive dataset with 29,100 events and 474 labels, OSCAR recovers interpretable causal structures in minutes, while classical methods fail to scale, enabling practical scientific diagnostics at production scale. |
| title | One-Shot Multi-Label Causal Discovery in High-Dimensional Event Sequences |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.23213 |