Kernel-based Conditional Independence Test and Application in Causal Discovery

Understand

Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery.

• Due to the curse of dimensionality, testing for conditional independence of continuous variables is particularly challenging.
• We propose a Kernel-based Conditional Independence test (KCI-test), by constructing an appropriate test statistic and deriving its asymptotic distribution under the null hypothesis of conditional independence.
• The proposed method is computationally efficient and easy to implement.