Definite Non-Ancestral Relations and Structure Learning
This work addresses a bottleneck in causal inference for researchers by providing incremental improvements to existing structure learning methods.
The paper tackles the problem of learning causal DAGs by proposing a framework to infer definite non-ancestral relations directly, without first learning the skeleton, which improves efficiency in structure learning algorithms.
In causal graphical models based on directed acyclic graphs (DAGs), directed paths represent causal pathways between the corresponding variables. The variable at the beginning of such a path is referred to as an ancestor of the variable at the end of the path. Ancestral relations between variables play an important role in causal modeling. In existing literature on structure learning, these relations are usually deduced from learned structures and used for orienting edges or formulating constraints of the space of possible DAGs. However, they are usually not posed as immediate target of inference. In this work we investigate the graphical characterization of ancestral relations via CPDAGs and d-separation relations. We propose a framework that can learn definite non-ancestral relations without first learning the skeleton. This frame-work yields structural information that can be used in both score- and constraint-based algorithms to learn causal DAGs more efficiently.