Generalized score with cross validation

Generalized score with cross validation for single-dimensional variables

Calculate the local score using negative k-fold cross-validated log likelihood as the score, based on a regression model in RKHS [1].

Usage

from causallearn.score.LocalScoreFunction import local_score_cv_general
score = local_score_cv_general(Data, Xi, PAi, parameters)

Parameters

Data: (sample, features).

Xi: current index.

PAi: parent indexes.

parameters:

• kfold: the fold number in cross validation.

• lambda: regularization parameter.

Returns

score: Local score.

Generalized score with cross validation for multi-dimensional variables

Calculate the local score using negative k-fold cross-validated log likelihood as the score, based on a regression model in RKHS for data with multi-dimensional variables [1].

Usage

from causallearn.score.LocalScoreFunction import local_score_cv_multi
score = local_score_cv_multi(Data, Xi, PAi, parameters)

Parameters

Data: (sample, features).

Xi: current index.

PAi: parent indexes.

parameters:

• kfold: the fold number in cross validation.

• lambda: regularization parameter.

• dlabel: indicate the data dimensions that belong to each variable. It is only used when the variables have multivariate dimensions.

Returns

score: Local score.

[1] (1,2)

Huang, B., Zhang, K., Lin, Y., Schölkopf, B., & Glymour, C. (2018, July). Generalized score functions for causal discovery. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1551-1560).