DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms
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 represen...
| Authors | Yikang Chen, Zhengkang Guan, Haoyuan Qian et al. |
|---|---|
| Primary category | cs.LG |
| Published | 13 July 2026 |
| Last updated at source | 2 August 2026 |
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