Framework for independence testing and conditional independence testing, with multiprocessing. Currently uses mutual information (MI) and conditional mutual information (CMI) as test statistics, estimated using k-NN methods. Also supports a routine for Markov blanket feature selection. Reports permutation-based p-values.

pip install pycit

• ksg_mi: k-NN estimator for continuous data
• bi_ksg_mi: "bias-improved" k-NN estimator for continuous data
• mixed_mi: k-NN estimator for discrete-continuous mixtures

• ksg_cmi: k-NN estimator for continuous data
• bi_ksg_cmi: "bias-improved" k-NN estimator for continuous data
• mixed_cmi: k-NN estimator for discrete-continuous mixtures

Note: Also includes a differential entropy estimator: kl_entropy.

from pycit import itest

# Test whether or not x and y are independent
pval = itest(x, y, test_args={'statistic': 'ksg_mi', 'n_jobs': 2})
is_independent = (pval >= 1.- confidence_level)

from pycit import citest

# Test whether or not x and y are conditionally independent given z
pval = citest(x, y, z, test_args={'statistic': 'ksg_mi', 'n_jobs': 2})
is_conditionally_independent = (pval >= 1.- confidence_level)

from pycit.markov_blanket import MarkovBlanket

# specify CI test configuration
cit_funcs = {
    'it_args': {
        'test_args': {
            'statistic': 'ksg_mi',
            'n_jobs': 2
        }
    },
    'cit_args': {
        'test_args': {
            'statistic': 'ksg_cmi',
            'n_jobs': 2
        }
    }
}

# find Markov blanket of Y. x_data contains data from predictor variables, X_1,...,X_m
mb = MarkovBlanket(x_data, y_data, cit_funcs)
markov_blanket = mb.find_markov_blanket()

• numpy
• scipy
• scikit-learn

• Kozachenko, L. and Leonenko, N. (1987). Sample estimate of the entropy of a random vector. Problemy Peredachi Informatsii, 23(2):9–16.
• Kraskov, A., Stögbauer, H., and Grassberger, P. (2004). Estimating mutual information. Physical Review E, 69(6):066138.
• Frenzel, S. and Pompe, B. (2007). Partial mutual information for coupling analysis of multivariate time series. Physical Review Letters, 99(20):204101.
• Gao, W., Kannan, S., Oh, S., and Viswanath, P. (2017). Estimating mutual information for discrete-continuous mixtures. In NIPS'2017.
• Gao, W., Oh, S., and Viswanath, P. (2018). Demystifying fixed k-nearest neighbor information estimators. IEEE Transactions on Information Theory, 64(8):5629–5661.
• Runge, J. (2018). Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. In AISTATS'18.
• Yang, A., Ghassami, A., Raginsky, M., Kiyavash, N., and Rosenbaum, E. (2020). Model-Augmented Estimation of Conditional Mutual Information for Feature Selection. In UAI'2020.