大型因果模型在时序因果发现中的应用

摘要

传统上，无论是针对横截面数据还是时间序列数据的因果发现，都遵循数据集特定的范式，即为每个独立数据集单独拟合新模型。这种方法限制了多数据集预训练的潜力。大型因果模型（LCMs）的概念设想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，随着输入规模增大会出现性能衰减，且严重依赖合成数据，限制了泛化能力。我们提出了一个基于原理的LCM框架，将多样化的合成数据生成器与真实时间序列数据集相结合，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能有效扩展到更高变量数量和更深层架构，同时保持强劲性能。与经典方法和神经基线相比，经过训练的模型实现了具有竞争力或更优的准确度，尤其在分布外场景下表现突出，同时支持快速单次推理。结果表明LCM为时序因果发现提供了有前景的基础模型范式。实验数据和模型权重详见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/.

大型因果模型在时序因果发现中的应用

Large Causal Models for Temporal Causal Discovery

摘要

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