Learning causal structure from observational data has attracted much attention, and it is notoriously challenging to find the underlying structure in the presence of confounders (hidden direct common causes of two variables).

Interest in this problem derives from its applications to Bayesian inference and differential privacy.

Neural Information Processing Systems http://nips.cc/

This allows us to depict richer global behaviors of SAM.