Causal discovery of linear acyclic models with arbitrary distributions

Understand

An important task in data analysis is the discovery of causal relationships between observed variables.

• For continuous-valued data, linear acyclic causal models are commonly used to model the data-generating process, and the inference of such models is a well-studied problem.
• However, existing methods have significant limitations.
• Methods based on conditional independencies (Spirtes et al.