RCD

class lingam.RCD(max_explanatory_num=2, cor_alpha=0.01, ind_alpha=0.01, shapiro_alpha=0.01, MLHSICR=False, bw_method='mdbs')

Implementation of RCD Algorithm [1]

References

[1]	T.N.Maeda and S.Shimizu. RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders. In Proc. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS2020), Palermo, Sicily, Italy. PMLR 108:735-745, 2020.

__init__(max_explanatory_num=2, cor_alpha=0.01, ind_alpha=0.01, shapiro_alpha=0.01, MLHSICR=False, bw_method='mdbs')

Construct a RCD model.

Parameters:

• max_explanatory_num (int, optional (default=2)) – Maximum number of explanatory variables.
• cor_alpha (float, optional (default=0.01)) – Alpha level for pearson correlation.
• ind_alpha (float, optional (default=0.01)) – Alpha level for HSIC.
• shapiro_alpha (float, optional (default=0.01)) – Alpha level for Shapiro-Wilk test.
• MLHSICR (bool, optional (default=False)) – If True, use MLHSICR for multiple regression, if False, use OLS for multiple regression.
• bw_method (str, optional (default=``mdbs``)) –

  The method used to calculate the bandwidth of the HSIC.

  • mdbs : Median distance between samples.
  • scott : Scott’s Rule of Thumb.
  • silverman : Silverman’s Rule of Thumb.

adjacency_matrix_

Estimated adjacency matrix.

Returns:adjacency_matrix_ – The adjacency matrix B of fitted model, where n_features is the number of features. Set np.nan if order is unknown. Return type:array-like, shape (n_features, n_features)

ancestors_list_

Estimated ancestors list.

Returns:ancestors_list_ – The list of causal ancestors sets, where n_features is the number of features. Return type:array-like, shape (n_features)

bootstrap(X, n_sampling)

Evaluate the statistical reliability of DAG based on the bootstrapping.

Parameters:

• X (array-like, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.
• n_sampling (int) – Number of bootstrapping samples.

Returns:

result – Returns the result of bootstrapping.

Return type:

BootstrapResult

fit(X)

Fit the model to X.

Parameters:X (array-like, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features. Returns:self – Returns the instance itself. Return type:object

get_error_independence_p_values(X)

Calculate the p-value matrix of independence between error variables.

Parameters:X (array-like, shape (n_samples, n_features)) – Original data, where n_samples is the number of samples and n_features is the number of features. Returns:independence_p_values – p-value matrix of independence between error variables. Return type:array-like, shape (n_features, n_features)