A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data

Understand

Many relations of scientific interest are nonlinear, and even in linear systems distributions are often non-Gaussian, for example in fMRI BOLD data.

• A class of search procedures for causal relations in high dimensional data relies on sample derived conditional independence decisions.
• The most common applications rely on Gaussian tests that can be systematically erroneous in nonlinear non-Gaussian cases.
• Recent work (Gretton et al.