$\text{seq2cause}$: A Unified Framework for Causal Discovery in Event Sequences

Complex systems such as vehicles, patients, 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 $\text{seq2cause}$, 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 (29K event types, 474 failure outcomes), $\text{seq2cause}$ 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.