We consider constraint-based methods for causal structure learning, such as the PC-, FCI-,
RFCI-and CCD-algorithms (Spirtes et al., 1993, 2000; Richardson, 1996; Colombo et al …

Discovering causal relationships from observational data is a crucial problem and it has
applications in many research areas. The PC algorithm is the state-of-the-art constraint …

ABSTRACT PC is a prototypical constraint-based algorithm for learning Bayesian networks,
a special case of directed acyclic graphs. An existing variant of it, in the R package pcalg …

We compare measures of structural distance between both, Bayesian networks and
equivalence classes of Bayesian networks. The main application of these measures is in …

This paper is concerned with various combinatorial parameters of classes that can be
learned from a small set of examples. We show that the recursive teaching dimension …

In Bayesian network structure learning, the quality of the directed graph learned by the
constraint-based approaches can be greatly affected by the order of choosing variable pairs …

Bayesian networks (BNs) are a widely used graphical model in machine learning. As
learning the structure of BNs is NP-hard, high-performance computing methods are …

Background The reconstruction of reliable graphical models from observational data is
important in bioinformatics and other computational fields applying network reconstruction …

Learning the causal structures in high-dimensional datasets allows deriving advanced
insights from observational data, thus creating the potential for new applications. One crucial …

This paper presents an extension to the Conservative PC algorithm which is able to detect
violations of adjacency faithfulness under causal sufficiency and triangle faithfulness …