A fast PC algorithm for high dimensional causal discovery with multi-core PCs

Understand

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 based method for causal discovery.
• However, runtime of the PC algorithm, in the worst-case, is exponential to the number of nodes (variables), and thus it is inefficient when being applied to high dimensional data, e.g.
• gene expression datasets.