用弱先验知识提升因果发现模型的实用性与鲁棒性
A Knowledge-Informed Pretrained Model for Causal Discovery
- 通过双源编码器-解码器结构融合观测数据与弱先验知识
- 在多种数据分布下优于现有基线，对不同图密度和变量规模均表现稳定
- 适合缺乏强干预数据但有领域常识的科研与工业场景
因果发现虽被广泛研究，但多数方法依赖强假设，或过度依赖昂贵的干预信号与部分真实标签作为强先验，或完全数据驱动而缺乏指导，限制了实际应用。针对现实中仅具备粗粒度领域知识的情况，我们提出一种知识引导的预训练因果发现模型，将弱先验作为合理中间路径。模型采用双源编码器-解码器架构，以知识感知方式处理观测数据。设计多样化预训练数据集与课程学习策略，使模型能平滑适应不同先验强度、图密度及变量规模下的机制变化。在分布内、分布外及真实数据集上的大量实验表明，该模型持续优于现有基线，具备强鲁棒性与实用价值。
原文摘要 · Abstract (English)
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.
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