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.