这篇论文提出了GENESIS，让因果发现的每条边都能说清是统计证据还是领域知识，还保证100%可追溯，比纯统计方法更准。
GENESIS是一个可解释的混合因果发现框架，将图构建分解为可解释的决策点。它先识别并评分三元结构（链、叉、对撞）作为透明先验，再结合观测证据逐步精化图，仅在统计证据不足时调用领域知识。实验显示，GENESIS在所有设置中实现100%的决策可追溯性，并在多数基准数据集上以结构汉明距离（SHD）优于纯统计方法，性能与最先进的LLM辅助方法相当。
GENESIS: Towards Explainable Causal Discovery
Causal Discovery (CD) from observational data faces two fundamental challenges. First, purely statistical methods often lack the power to resolve structural ambiguities in low-sample regimes. Second, although LLM-assisted hybrid approaches improve structure recovery through semantic reasoning, the influence of that reasoning on individual edge decisions remains largely opaque. Consequently, existing hybrid methods fail to satisfy a fundamental requirement: explaining why a particular edge is included or excluded in the learned directed acyclic graph (DAG). This is critical in real-world applications, where no ground-truth DAG exists and every structural decision must be independently justified. We formalize this requirement as decision traceability, requiring every inferred edge to be supported by auditable statistical evidence, Markov Blanket consistency, or explicit domain reasoning. We propose GENESIS, an explainable hybrid CD framework that decomposes graph construction into interpretable decision points. GENESIS first identifies and scores three-node structural motifs, including chains, forks, and colliders, to establish transparent structural priors, then progressively refines the graph by integrating these priors with observational evidence, invoking domain knowledge only when statistical evidence is insufficient. By design, every edge decision is resolved through an auditable source of evidence. Experiments show that GENESIS achieves 100% decision traceability across all settings, establishing explainability as a first-class objective in causal discovery. Despite this additional requirement, GENESIS consistently outperforms purely statistical CD methods on the majority of benchmark datasets across all sample regimes in terms of Structural Hamming Distance (SHD), while achieving performance comparable to state-of-the-art LLM-assisted approaches.