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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.01135 |
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| _version_ | 1866910055810465792 |
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| author | Math, Hugo Lienhart, Rainer |
| author_facet | Math, Hugo Lienhart, Rainer |
| contents | We study causal discovery from a single observed sequence of discrete events generated by a stochastic process, as encountered in vehicle logs, manufacturing systems, or patient trajectories. This regime is particularly challenging due to the absence of repeated samples, high dimensionality, and long-range temporal dependencies of the single observation during inference. We introduce TRACE, a scalable framework that repurposes autoregressive models as pretrained density estimators for conditional mutual information estimation. TRACE infers the summary causal graph between event types in a sequence, scaling linearly with the event vocabulary and supporting delayed causal effects, while being fully parallel on GPUs. We establish its theoretical identifiability under imperfect autoregressive models. Experiments demonstrate robust performance across different baselines and varying vocabulary sizes including an application to root-cause analysis in vehicle diagnostics with over 29,100 event types. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01135 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable Sample-Level Causal Discovery in Event Sequences via Autoregressive Density Estimation Math, Hugo Lienhart, Rainer Machine Learning We study causal discovery from a single observed sequence of discrete events generated by a stochastic process, as encountered in vehicle logs, manufacturing systems, or patient trajectories. This regime is particularly challenging due to the absence of repeated samples, high dimensionality, and long-range temporal dependencies of the single observation during inference. We introduce TRACE, a scalable framework that repurposes autoregressive models as pretrained density estimators for conditional mutual information estimation. TRACE infers the summary causal graph between event types in a sequence, scaling linearly with the event vocabulary and supporting delayed causal effects, while being fully parallel on GPUs. We establish its theoretical identifiability under imperfect autoregressive models. Experiments demonstrate robust performance across different baselines and varying vocabulary sizes including an application to root-cause analysis in vehicle diagnostics with over 29,100 event types. |
| title | Scalable Sample-Level Causal Discovery in Event Sequences via Autoregressive Density Estimation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.01135 |