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Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing.
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A consistent characteristic function-based test for conditional independence
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Permutation Testing Improves Bayesian Network Learning , pages 322–337
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Kernel-based conditional independence test and application in causal discovery
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The randomized dependence coefficient
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Randomized nonlinear component analysis
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Random features for large-scale kernel machines
A. Rahimi and B. Recht · 2007
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Kernel measures of conditional dependence
K. Fukumizu, A. Gretton, X. Sun, and B. Schölkopf · 2008
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A kernel statistical test of independence
A. Gretton, K. Fukumizu, C. Teo, L. Song, B. Schölkopf, and A. Smola · 2008
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A nonparametric hellinger metric test for conditional independence
L. Su and H. White · 2008
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On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias
J. Zhang · 2008
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Testing conditional independence using maximal nonlinear conditional correlation
T.-M. Huang · 2010
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D. Lopez-Paz, S. Sra, A. Smola, Z. Ghahramani, and B. Schölkopf · 2014
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A scalable conditional independence test for nonlinear, non-gaussian data
J. D. Ramsey · 2014
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momentchi2
D. Bodenham · 2015
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Cognition and aging in the usa (cogusa), 2007-2009, 2015
J. McArdle, W. Rodgers, and R. Willis · 2015
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On the error of random fourier features
D. J. Sutherland and J. G. Schneider · 2015
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A comparison of efficient approximations for a weighted sum of chi-squared random variables
D. Bodenham and N. Adams · 2016
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