Kernel-based conditional independence (KCI) test and independence test

Kernel-based conditional independence (KCI) test and independence test 1. To test if x and y are conditionally or unconditionally independent on Z. For unconditional independence tests, Z is set to the empty set.

Usage

from causallearn.utils.cit import kci
p = kci(data, X, Y, condition_set, kernelX, kernelY, kernelZ, est_width, polyd, kwidthx, kwidthy, kwidthz)

Parameters

data: numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples and n_features is the number of features.

X, Y, and condition_set: column indices of data. condition_set could be None.

KernelX/Y/Z (condition_set): [‘GaussianKernel’, ‘LinearKernel’, ‘PolynomialKernel’]. (For ‘PolynomialKernel’, the default degree is 2. Currently, users can change it by setting the ‘degree’ of ‘class PolynomialKernel()’.

est_width: set kernel width for Gaussian kernels.

‘empirical’: set kernel width using empirical rules (default).
‘median’: set kernel width using the median trick.

polyd: polynomial kernel degrees (default=2).

kwidthx: kernel width for data x (standard deviation sigma).

kwidthy: kernel width for data y (standard deviation sigma).

kwidthz: kernel width for data z (standard deviation sigma).

Returns

p: the p value.

1: Zhang, K., Peters, J., Janzing, D., & Schölkopf, B. (2011, July). Kernel-based Conditional Independence Test and Application in Causal Discovery. In 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011) (pp. 804-813). AUAI Press.