Heterogeneous graph neural networks have become popular in various domains.
However, their generalizability and interpretability are limited due to the
discrepancy between their inherent inference flows and human reasoning logic or
underlying causal relationships for the learning problem. This study introduces
a novel solution, HG-SCM (Heterogeneous Graph as Structural Causal Model). It
can mimic the human perception and decision process through two key steps:
constructing intelligible variables based on semantics derived from the graph
schema and automatically learning task-level causal relationships among these
variables by incorporating advanced causal discovery techniques. We compared
HG-SCM to seven state-of-the-art baseline models on three real-world datasets,
under three distinct and ubiquitous out-of-distribution settings. HG-SCM
achieved the highest average performance rank with minimal standard deviation,
substantiating its effectiveness and superiority in terms of both predictive
power and generalizability. Additionally, the visualization and analysis of the
auto-learned causal diagrams for the three tasks aligned well with domain
knowledge and human cognition, demonstrating prominent interpretability.
HG-SCM's human-like nature and its enhanced generalizability and
interpretability make it a promising solution for special scenarios where
transparency and trustworthiness are paramount.

通过构建可理解的变量和包含高级因果发现技术的自动学习任务级因果关系，HG-SCM 可以模拟人类感知和决策过程。在实证数据集上与七种基线模型进行比较后，HG-SCM 取得了最高的平均性能排名和最小的标准差，充分证明了其在预测能力和推广性方面的有效性和优越性，并且所自动学习的三个任务的因果图可视化和分析与领域知识和人类认知相吻合，展示出了其卓越的可解释性。HG-SCM 的类人特性以及增强的推广性和可解释性使其成为在透明度和可信度至关重要的特殊场景中具有潜力的解决方案。