Definite Non-Ancestral Relations and Structure Learning
- URL: http://arxiv.org/abs/2105.10350v1
- Date: Thu, 20 May 2021 06:53:52 GMT
- Title: Definite Non-Ancestral Relations and Structure Learning
- Authors: Wenyu Chen, Mathias Drton and Ali Shojaie
- Abstract summary: 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.
- Score: 10.8738893134525
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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