关键词: 关键词: 关系因果模型, 结构学习, 因果发现, 关系型数据, 混合算法

Abstract: Abstract: Relationships in the real world involve interactions among various entity types, and Relational Causal Model (RCM) provides a clear depiction of such relationships. Learning causal relationships from relational causal model is crucial for supporting business decision-making in complex scenarios. Most existing algorithms rely on the oracle relational conditional independence to discover causal relationships, failing to learn from relational data; algorithms designed to learn causal dependencies from relational data typically adopt constraint-based approaches, but their performance is limited by finite sample sizes, resulting in relatively low recall and F1 score. To address these issues, a hybrid algorithm based on constraint and scoring (RCSH) was proposed. Undirected dependencies were first identified using a heuristic algorithm, and an undirected relational causal model was constructed. The Relational Bivariate Orientation (RBO) rule was then applied to orient the model. After the search space was restricted, a greedy hill-climbing algorithm was employed to improve sensitivity to long relational paths and multi-attribute dependencies under limited sample sizes. In the comparison experiments with Robust Relational Causal Discovery (RRCD), the proposed algorithm achieved improvements of approximately 12.8% in recall and 3.31% in F1-score, showing a steady upward trend as the dataset size increased. Furthermore, the applicability and effectiveness of RCSH were validated on real-world datasets.

Key words: Keywords: relational causal model, structure learning, causal discovery, relational data, hybrid algorithm