发表机构
Zhejiang University; Tsinghua University（浙江大学; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对观测表格数据因果发现难题，提出DAG-FM基础模型，通过自回归阶段、表格交互块及叶专家混合处理复杂情况，经迭代推理构建DAG，在合成及真实数据集上性能优于传统算法和其他模型。
AI 中文摘要
从观测表格数据中进行因果发现具有根本挑战性，原因在于潜在因果机制的异质性和有向无环图（DAG）的高维组合搜索空间。本文提出了DAG-FM，一种摊销因果发现的新型基础模型架构。它将因果发现过程分解为两个自回归阶段，采用基于Transformer的子模块。为有效建模复杂交互，采用稳健表格交互块。还引入叶专家混合（MoLE）处理多样未知假设，通过迭代推理算法提取因果顺序并构建有效DAG。实验表明DAG-FM在合成基准和真实世界数据集上性能达最优。
英文摘要
Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs). In this paper, we propose \textbf{DAG-FM}, a novel foundation model architecture that amortizes causal discovery. Unlike direct matrix prediction, DAG-FM decomposes the causal discovery process into two auto-regressive stages using two specialized Transformer-based sub-modules: a leaf-node predictor and a parent-node predictor. To effectively model complex row-column interactions, we adopt a robust tabular interaction block to output feature-wise representations. Crucially, to handle diverse and unknown Functional Causal Model (FCM) assumptions in real-world scenarios, we introduce Mixture-of-Leaf-Experts (MoLE), allowing the model to dynamically route and adapt to identifiable mechanism families. Through an iterative inference algorithm, DAG-FM seamlessly extracts causal orderings and constructs valid DAGs. Extensive experiments demonstrate that DAG-FM achieves state-of-the-art performance on both synthetic benchmarks and complex real-world datasets, significantly outperforming traditional classical algorithms and recent foundation models in both accuracy and scalability.
Comments33 pages, 9 figures, 12 tables, preprint
