  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.
