seq2cause: One Autoregressive Backbone, Four Causal Discovery Tasks in Event Sequences

arXiv:2609.31801v1 Announce Type: new Abstract: Complex systems such as vehicles, patients, or genomes emit discrete event sequences whose operative question is causal, not predictive: which events cause which other events, and which cause higher-level outcomes such as failures or diseases? This question decomposes along two axes -- dependency type (event $\to$ event vs.\ event $\to$ outcome) and causal scope (single sequence vs.\ population) -- yielding four structurally distinct regimes with different identifiability conditions. No existing method addresses more than one, because all assume multi-stream structure with low vocabulary, and none scales beyond a few hundred event types. We present \textsc{Seq2Cause}, a unified framework that resolves all four regimes through a single shared primitive: a pretrained autoregressive model repurposed as an amortized conditional independence testing engine requiring no task-specific retraining. We establish a prediction--causality duality: the model's excess cross-entropy simultaneously bounds causal identification error across all four regimes, so that every improvement in next-token prediction tightens causal guarantees for free. On nonlinear SCMs (vocabularies up to $8{,}000$ types) and real-world vehicle diagnostic logs ($29$K event types, $474$ failure outcomes), \textsc{Seq2Cause} is the first method to populate all four regimes at scale with a single frozen backbone. Existing methods are either inapplicable, inaccurate, or computationally intractable in this setting.

Abstract:Complex systems such as vehicles, patients, or genomes emit discrete event sequences whose operative question is causal, not predictive: which events cause which other events, and which cause higher-level outcomes such as failures or diseases? This question decomposes along two axes -- dependency type (event $\to$ event vs.\ event $\to$ outcome) and causal scope (single sequence vs.\ population) -- yielding four structurally distinct regimes with different identifiability conditions. No existing method addresses more than one, because all assume multi-stream structure with low vocabulary, and none scales beyond a few hundred event types. We present \textsc{Seq2Cause}, a unified framework that resolves all four regimes through a single shared primitive: a pretrained autoregressive model repurposed as an amortized conditional independence testing engine requiring no task-specific retraining. We establish a prediction--causality duality: the model's excess cross-entropy simultaneously bounds causal identification error across all four regimes, so that every improvement in next-token prediction tightens causal guarantees for free. On nonlinear SCMs (vocabularies up to $8{,}000$ types) and real-world vehicle diagnostic logs ($29$K event types, $474$ failure outcomes), \textsc{Seq2Cause} is the first method to populate all four regimes at scale with a single frozen backbone. Existing methods are either inapplicable, inaccurate, or computationally intractable in this setting.

Machine Learning (cs.LG)

arXiv:2609.31801 [cs.LG]

(or arXiv:2609.31801v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.31801 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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seq2cause: One Autoregressive Backbone, Four Causal Discovery Tasks in Event Sequences

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