AI 中文总结
针对事件序列中四种因果发现任务，提出统一框架Seq2Cause，利用预训练自回归模型作为条件独立性检验引擎，通过预测-因果二元性，以单一冻结主干在规模上解决所有机制。
AI 中文摘要
车辆、患者或基因组等复杂系统会发出离散事件序列，其核心问题在于因果而非预测：哪些事件导致其他事件，哪些事件导致更高层次的结果（如故障或疾病）？该问题沿两个轴分解——依赖类型（事件到事件 vs. 事件到结果）和因果范围（单序列 vs. 群体）——产生四种结构上不同的机制，具有不同的可识别性条件。现有方法均无法处理超过一种机制，因为它们都假设具有低词汇量的多流结构，且无法扩展到数百种事件类型以上。我们提出 Seq2Cause，一个统一框架，通过一个共享的原始组件解决所有四种机制：一个预训练的自回归模型，被重新用作摊销的条件独立性检验引擎，无需针对特定任务进行重新训练。我们建立了一个预测-因果二元性：模型的超额交叉熵同时限制了所有四种机制中的因果识别误差，因此下一次词元预测的每一次改进都会免费收紧因果保证。在非线性结构因果模型（词汇量高达 8,000 种类型）和真实世界的车辆诊断日志（29K 事件类型，474 种故障结果）上，Seq2Cause 是第一个使用单个冻结主干在规模上填充所有四种机制的方法。现有方法在这种情况下要么不适用，要么不准确，要么计算上不可行。
英文摘要
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.
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