A Knowledge-Informed Pretrained Model for Causal Discovery
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908905388376064 |
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| author | Xu, Wenbo He, Yue Wang, Yunhai Zhang, Xingxuan Kuang, Kun Chen, Yueguo Cui, Peng |
| author_facet | Xu, Wenbo He, Yue Wang, Yunhai Zhang, Xingxuan Kuang, Kun Chen, Yueguo Cui, Peng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20842 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Knowledge-Informed Pretrained Model for Causal Discovery Xu, Wenbo He, Yue Wang, Yunhai Zhang, Xingxuan Kuang, Kun Chen, Yueguo Cui, Peng Machine Learning 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. |
| title | A Knowledge-Informed Pretrained Model for Causal Discovery |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.20842 |