A Knowledge-Informed Pretrained Model for Causal Discovery

Causal discovery has been widely studied, yet many existing methods rely on restrictive assumptions and occupy one of two extremes: they either rely on strong auxiliary supervision, such as costly interventional signals or partial ground-truth, or adopt purely data-driven paradigms without guidance, which hinders practical deployment. Motivated by real-world scenarios where coarse domain knowledge is available, we propose a knowledge-informed pretrained model for causal discovery that integrates weak prior knowledge as a middle ground between the two extremes. Our model adopts a dual-source encoder-decoder architecture to jointly process observational data and structural knowledge while preserving their distinct semantics. We design a diverse pretraining dataset and a curriculum learning strategy that smoothly adapts the model in a knowledge-informed way 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, while exhibiting robust performance across diverse settings and promising practical applicability.