Causal discovery is essential for advancing data-driven fields such as scientific AI and data analysis, yet existing approaches face significant time- and space-efficiency bottlenecks when scaling to large graphs. To address this challenge, we present CauScale, a neural architecture designed for efficient causal discovery that scales inference to graphs with up to 1000 nodes. CauScale improves time efficiency via a reduction unit that compresses data embeddings and improves space efficiency by adopting tied attention weights to avoid maintaining axis-specific attention maps. To keep high causal discovery accuracy, CauScale adopts a two-stream design: a data stream extracts relational evidence from high-dimensional observations, while a graph stream integrates statistical graph priors and preserves key structural signals. CauScale successfully scales to 500-node graphs during training, where prior work fails due to space limitations. Across testing data with varying graph scales and causal mechanisms, CauScale achieves 99.6% mAP on in-distribution data and 84.4% on out-of-distribution data, while delivering 4-13,000 times inference speedups over prior methods. Our project page is at https://github.com/OpenCausaLab/CauScale.

翻译：因果发现对于推动科学AI和数据分析等数据驱动领域至关重要，然而现有方法在扩展到大规模图结构时面临显著的时间和空间效率瓶颈。为应对这一挑战，我们提出CauScale——一种专为高效因果发现设计的神经架构，可将推理扩展至包含多达1000个节点的图结构。CauScale通过压缩数据嵌入的约简单元提升时间效率，并采用共享注意力权重避免维护轴特定注意力图以提升空间效率。为保持高精度因果发现能力，CauScale采用双流设计：数据流从高维观测中提取关系证据，图流则整合统计图先验并保留关键结构信号。CauScale在训练阶段成功扩展至500节点图结构（先前工作因空间限制无法实现），在涵盖不同图规模和因果机制的测试数据中，CauScale在分布内数据达到99.6% mAP，分布外数据达到84.4% mAP，同时推理速度较现有方法提升4-13,000倍。项目页面详见https://github.com/OpenCausaLab/CauScale。