[2603.20842] A Knowledge-Informed Pretrained Model for Causal Discovery

Computer Science > Machine Learning arXiv:2603.20842 (cs) [Submitted on 21 Mar 2026] Title:A Knowledge-Informed Pretrained Model for Causal Discovery Authors:Wenbo Xu, Yue He, Yunhai Wang, Xingxuan Zhang, Kun Kuang, Yueguo Chen, Peng Cui View a PDF of the paper titled A Knowledge-Informed Pretrained Model for Causal Discovery, by Wenbo Xu and 6 other authors View PDF HTML (experimental) Abstract:Causal discovery has been widely studied, yet many existing methods rely on strong assumptions or fall into two extremes: either depending on costly interventional signals or partial ground truth as strong priors, or adopting purely data driven paradigms with limited guidance, which hinders practical deployment. Motivated by real-world scenarios where only coarse domain knowledge is available, we propose a knowledge-informed pretrained model for causal discovery that integrates weak prior knowledge as a principled middle ground. Our model adopts a dual source encoder-decoder architecture to process observational data in a knowledge-informed way. We design a diverse pretraining dataset and a curriculum learning strategy that smoothly adapts the model to varying prior strengths across mechanisms, graph densities, and variable scales. Extensive experiments on in-distribution, out-of distribution, and real-world datasets demonstrate consistent improvements over existing baselines, with strong robustness and practical applicability. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603...