面向时序因果发现的大型因果模型

摘要

传统上，针对横截面与时间序列数据的因果发现一直遵循数据集特定范式，即每个独立数据集都需拟合新模型。这种方法限制了多数据集预训练的潜力。大型因果模型（LCMs）的概念提出了一类专门为时序因果发现设计的预训练神经架构。现有方法受限于较小变量规模，随输入增大性能下降，且严重依赖合成数据，制约了泛化能力。我们提出一个理论严谨的LCM框架，将多样化合成生成器与真实时序数据集相结合，实现规模化学习。在合成、半合成及真实基准测试上的大量实验表明，LCM能有效扩展至更高变量数量和更深层架构，同时保持强大性能。与经典及神经基线方法相比，训练后的模型在分布外场景中尤其展现出竞争优势，且支持快速单次推理。实验结果证明LCM是时序因果发现中极具前景的基础模型范式。实验数据与模型权重详见https://github.com/kougioulis/LCM-paper/。

English

Causal discovery for both cross-sectional and temporal data has traditionally followed a dataset-specific paradigm, where a new model is fitted for each individual dataset. Such an approach limits the potential of multi-dataset pretraining. The concept of large causal models (LCMs) envisions a class of pre-trained neural architectures specifically designed for temporal causal discovery. Prior approaches are constrained to small variable counts, degrade with larger inputs, and rely heavily on synthetic data, limiting generalization. We propose a principled framework for LCMs, combining diverse synthetic generators with realistic time-series datasets, allowing learning at scale. Extensive experiments on synthetic, semi-synthetic and realistic benchmarks show that LCMs scale effectively to higher variable counts and deeper architectures while maintaining strong performance. Trained models achieve competitive or superior accuracy compared to classical and neural baselines, particularly in out-of-distribution settings, while enabling fast, single-pass inference. Results demonstrate LCMs as a promising foundation-model paradigm for temporal causal discovery. Experiments and model weights are available at https://github.com/kougioulis/LCM-paper/.

面向时序因果发现的大型因果模型

Large Causal Models for Temporal Causal Discovery

摘要

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