Causal discovery algorithms aim at untangling complex causal relationships from data. Here, we study causal discovery and inference methods based on staged tree models, which can represent complex and asymmetric causal relationships between categorical variables. We provide a first graphical representation of the equivalence class of a staged tree, by looking only at a specific subset of its underlying independences. We further define a new pre-metric, inspired by the widely used structural intervention distance, to quantify the closeness between two staged trees in terms of their corresponding causal inference statements. A simulation study highlights the efficacy of staged trees in uncovering complexes, asymmetric causal relationships from data, and real-world data applications illustrate their use in practical causal analysis.

翻译：因果发现算法旨在从数据中解开复杂的因果关系。本文研究基于分层树模型的因果发现与推断方法，该类模型能够表示分类型变量间复杂且非对称的因果关系。通过仅关注其底层独立性的特定子集，我们首次提供了分层树等价类的图形表示。进一步地，受广泛使用的结构干预距离启发，我们定义了一种新的前度量，用以量化两个分层树在因果推断陈述方面的接近程度。模拟研究凸显了分层树在从数据中发现复杂、非对称因果关系方面的有效性，而真实世界数据应用则展示了其在实践因果分析中的用途。