prepare_data_copula_cpp_caus function

Generate (Gaussian) Copula MC samples for the causal setup with a single MC sample for each explicand

prepare_data_copula_cpp_caus(
  MC_samples_mat,
  x_explain_mat,
  x_explain_gaussian_mat,
  x_train_mat,
  S,
  mu,
  cov_mat
)

Arguments

• MC_samples_mat: arma::mat. Matrix of dimension (n_MC_samples, n_features) containing samples from the univariate standard normal.
• x_explain_mat: arma::mat. Matrix of dimension (n_explain, n_features) containing the observations to explain.
• x_explain_gaussian_mat: arma::mat. Matrix of dimension (n_explain, n_features) containing the observations to explain after being transformed using the Gaussian transform, i.e., the samples have been transformed to a standardized normal distribution.
• x_train_mat: arma::mat. Matrix of dimension (n_train, n_features) containing the training observations.
• S: arma::mat. Matrix of dimension (n_coalitions, n_features) containing binary representations of the used coalitions. S cannot contain the empty or grand coalition, i.e., a row containing only zeros or ones. This is not a problem internally in shapr as the empty and grand coalitions are treated differently.
• mu: arma::vec. Vector of length n_features containing the mean of each feature after being transformed using the Gaussian transform, i.e., the samples have been transformed to a standardized normal distribution.
• cov_mat: arma::mat. Matrix of dimension (n_features, n_features) containing the pairwise covariance between all pairs of features after being transformed using the Gaussian transform, i.e., the samples have been transformed to a standardized normal distribution.

Returns

An arma::cube/3D array of dimension (n_MC_samples, n_explain * n_coalitions, n_features), where the columns (,j,) are matrices of dimension (n_MC_samples, n_features) containing the conditional Gaussian copula MC samples for each explicand and coalition on the original scale.

Author(s)

Lars Henry Berge Olsen