CAM-UV

class lingam.CAMUV(alpha=0.01, num_explanatory_vals=2, independence='hsic', ind_corr=0.5)

Implementation of CAM-UV Algorithm [1]

References

[1]

T.N.Maeda and S.Shimizu. Causal additive models with unobserved variables. In Proc. Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI). PMLR 161:97-106, 2021.

__init__(alpha=0.01, num_explanatory_vals=2, independence='hsic', ind_corr=0.5)

Construct a CAM-UV model.

Parameters:

• alpha (float, optional (default=0.01)) – Alpha level.

• num_explanatory_vals (int, optional (default=2)) – Maximum number of explanatory variables.

• independence ({'hsic', 'fcorr'}, optional (default='hsic')) – Methods to determine independence. If ‘hsic’ is set, test for independence by HSIC. If ‘fcorr’ is set, independence is determined by F-correlation.

• ind_corr (float, optional (default=0.5)) – The threshold value for determining independence by F-correlation; independence is determined when the value of F-correlation is below this threshold value.

property adjacency_matrix_

Estimated adjacency matrix.

Returns:

adjacency_matrix_ – The adjacency matrix B of fitted model, where n_features is the number of features.

Return type:

array-like, shape (n_features, n_features)