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：原因发现算法旨在从数据中解开复杂的因果关系。 在这里, 我们研究基于分层树模型的因果发现和推断方法, 这些模型可以代表绝对变量之间的复杂和不对称因果关系。 我们通过只看分层其基本独立性,对分层树的等值类别提供了第一个图形表示。 我们还进一步定义了一个新的预测方法, 受广泛使用的结构性干预距离的启发, 以便用相应的因果推断说明来量化两个分层树之间的近切性。 模拟研究突出显示分层树在发现复杂物、数据中不对称因果关系以及现实世界数据应用中的功效, 说明它们在实际因果分析中的用途。</s>