Distribution-Free Learning of Bayesian Network Structure in Continuous Domains

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Kernel-based Conditional Independence Test and Application in Causal Discovery (2011, background): “Meth-ods in the second category, such as Margaritis (2005) and Huang (2010), discretize the conditioning set Z to a set of bins, and transform CI to the unconditional one in each bin.”
A Permutation-Based Kernel Conditional Independence Test (2014, methodology): “Other tests use this characterization by discretizing continuous conditioning variables and testing for independence within each discrete “bin” of Z (Margaritis, 2005).”
Minimax optimal conditional independence testing (2020, background): “From being a foundation of basic concepts such as sufficiency and ancillarity [11], to its applications in estimation and inference for graphical models [21, 23] and in causal inference and causal discovery [25, 31, 42], the concept of conditional independence and conditional independence testing pl”
Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery (2017, background): “One strategy involves discretizing continuous conditioning variables Z as ˘ Z in some optimal fashion and assessing unconditional in-dependence ∀ ˘ Z = ˘ z (Margaritis, 2005, Huang, 2010).”