Context-Specific Causal Discovery for Categorical Data Using Staged Trees
This work addresses causal discovery for categorical data with asymmetric relationships, which is important for fields like social sciences or medicine, but it appears incremental as it builds on existing staged tree frameworks.
The paper tackles the problem of discovering complex, asymmetric causal relationships from categorical data by developing new methods based on staged tree models, including a graphical representation for equivalence classes and a pre-metric for comparing causal inferences, with simulations showing efficacy in uncovering such relationships.
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.