Welcome to lingam’s documentation!

Contents:

• Installation Guide
• Tutorial
  • DirectLiNGAM
    • Model
    • Import and settings
    • Test data
    • Causal Discovery
    • Independence between error variables
  • Bootstrap
    • Import and settings
    • Test data
    • Bootstrapping
    • Causal Directions
    • Directed Acyclic Graphs
    • Probability
    • Total Causal Effects
    • Bootstrap Probability of Path
  • How to use prior knowledge in DirectLiNGAM
    • Import and settings
    • Utility function
    • Test data
    • Make Prior Knowledge Matrix
    • Causal Discovery
  • MultiGroupDirectLiNGAM
    • Model
    • Import and settings
    • Test data
    • Causal Discovery
    • Independence between error variables
    • Bootstrapping
    • Causal Directions
    • Directed Acyclic Graphs
    • Probability
    • Total Causal Effects
    • Bootstrap Probability of Path
  • Total Effect
  • Causal Effect on predicted variables
    • Identification of Feature with Greatest Causal Influence on Prediction
    • Estimation of Optimal Intervention
    • Use a known causal model
  • VARLiNGAM
    • Model
    • Import and settings
    • Test data
    • Causal Discovery
    • Independence between error variables
    • Bootstrap
      • Bootstrapping
    • Causal Directions
    • Directed Acyclic Graphs
    • Probability
    • Total Causal Effects
  • VARMALiNGAM
    • Model
    • Import and settings
    • Test data
    • Causal Discovery
    • Independence between error variables
    • Bootstrap
      • Bootstrapping
    • Causal Directions
    • Directed Acyclic Graphs
    • Probability
    • Total Causal Effects
  • Longitudinal LiNGAM
    • Model
    • Import and settings
    • Test data
    • Causal Discovery
    • Independence between error variables
    • Bootstrapping
    • Causal Directions
    • Directed Acyclic Graphs
    • Probability
    • Total Causal Effects
  • BottomUpParceLiNGAM
    • Model
    • Import and settings
    • Test data
    • Causal Discovery
    • Independence between error variables
    • Bootstrapping
    • Causal Directions
    • Directed Acyclic Graphs
    • Probability
    • Total Causal Effects
    • Bootstrap Probability of Path
  • How to use prior knowledge in BottomUpParceLiNGAM
    • Import and settings
    • Utility function
    • Test data
    • Make Prior Knowledge Matrix
    • Causal Discovery
  • RCD
    • Model
    • Import and settings
    • Test data
    • Causal Discovery
    • Independence between error variables
    • Bootstrapping
    • Causal Directions
    • Directed Acyclic Graphs
    • Probability
    • Total Causal Effects
    • Bootstrap Probability of Path
  • CAM-UV
    • Model
    • Import and settings
    • Test data
    • Causal Discovery
  • MultiGroupRCD
    • Import and settings
    • Test data
    • Causal Discovery
  • MultiGroupCAMUV
    • Import and settings
    • Data generation
    • Causal Discovery
  • MultiGroupRESIT
    • Import and settings
    • Test data
    • Causal Discovery
  • LiNA
    • Model
    • Import and settings
    • Single-domain test data
    • Causal Discovery for single-domain data
    • Multi-domain test data
    • Causal Discovery for multi-domain data
  • RESIT
    • Model
    • Import and settings
    • Test data
    • Causal Discovery
    • Bootstrapping
    • Causal Directions
    • Directed Acyclic Graphs
    • Probability
    • Bootstrap Probability of Path
  • How to use prior knowledge in RESIT
    • Import and settings
    • Utility function
    • Test data
    • Make Prior Knowledge Matrix
    • Causal Discovery
  • LiM
    • Model
    • Import and settings
    • Test data
    • Causal Discovery for linear mixed data
  • GroupLiNGAM
    • Model
    • Import and settings
    • Test data
    • Causal Discovery
    • Bootstrapping
    • Causal Directions
    • Directed Acyclic Graphs
    • Probability
    • Total Causal Effects
    • Bootstrap Probability of Path
  • GroupDirectLiNGAM
    • Import and settings
    • Test data
    • Causal Discovery
    • Bootstrapping
    • Causal Directions
    • Directed Acyclic Graphs
    • Probability
    • Total Causal Effects
    • Bootstrap Probability of Path
  • Finding ancestors of each variable
    • Import and settings
    • Test data
    • Extract the ancestors of each observed variable
  • F-correlation
    • Import and settings
    • Test data
    • Linear correlated data (Uncorrelated)
    • Linear correlated data (Strongly correlated)
    • Non-linear correlated data (Quadratic function)
    • Non-linear correlated data (Sin function)
  • Draw Causal Graph
    • Import and settings
    • Draw the result of LiNGAM
    • Save graph
    • Draw the result of LiNGAM with prediction model
    • Highlight paths between specified nodes
    • Draw the result of LiNGAM with emphasis on descendants and ancestors
    • Draw the result of Bootstrap with emphasis on descendants and ancestors
  • Visualization of nonlinear causal effect
    • Import and settings
    • Data generation
    • Causal discovery
    • Visualization
  • EvaluateModelFit
    • Import and settings
    • When all variables are continuous data
    • Test data
    • Causal Discovery
    • Evaluation
    • When the data has hidden common causes
    • Test data
    • Causal Discovery
    • When the data has ordinal variables
    • Test data
  • Bootstrap with imputation
    • Import and settings
    • Test data
    • Causal discovery with missing data
    • Checking results
      • Comparing obtained results with results for data with no missing
    • Getting bootstrap samples and imputed bootstrap samples
  • HighDimDirectLiNGAM
    • Import and settings
    • Test data
    • Causal Discovery
  • m-LiNGAM
    • Model
      • Assumptions
    • References
  • ABIC-LiNGAM
    • Model
    • Import and settings
    • Test data
    • Causal Discovery
  • Causal Explanations with LEWIS
    • Test data
    • Discretization
    • Model Training and Prediction with RandomForestClassifier
    • Computing LEWIS Scores
    • Visualization of LEWIS Scores Sorted by NeSuf
• API Reference
  • ICA-LiNGAM
    • ICALiNGAM
      • ICALiNGAM.__init__()
      • ICALiNGAM.adjacency_matrix_
      • ICALiNGAM.bootstrap()
      • ICALiNGAM.causal_order_
      • ICALiNGAM.estimate_total_effect()
      • ICALiNGAM.fit()
      • ICALiNGAM.get_error_independence_p_values()
  • DirectLiNGAM
    • DirectLiNGAM
      • DirectLiNGAM.__init__()
      • DirectLiNGAM.adjacency_matrix_
      • DirectLiNGAM.bootstrap()
      • DirectLiNGAM.causal_order_
      • DirectLiNGAM.estimate_total_effect()
      • DirectLiNGAM.fit()
      • DirectLiNGAM.get_error_independence_p_values()
  • MultiGroupDirectLiNGAM
    • MultiGroupDirectLiNGAM
      • MultiGroupDirectLiNGAM.__init__()
      • MultiGroupDirectLiNGAM.adjacency_matrices_
      • MultiGroupDirectLiNGAM.adjacency_matrix_
      • MultiGroupDirectLiNGAM.bootstrap()
      • MultiGroupDirectLiNGAM.causal_order_
      • MultiGroupDirectLiNGAM.estimate_total_effect()
      • MultiGroupDirectLiNGAM.estimate_total_effect2()
      • MultiGroupDirectLiNGAM.fit()
      • MultiGroupDirectLiNGAM.get_error_independence_p_values()
  • HighDimDirectLiNGAM
    • HighDimDirectLiNGAM
      • HighDimDirectLiNGAM.__init__()
      • HighDimDirectLiNGAM.adjacency_matrix_
      • HighDimDirectLiNGAM.bootstrap()
      • HighDimDirectLiNGAM.causal_order_
      • HighDimDirectLiNGAM.estimate_total_effect()
      • HighDimDirectLiNGAM.fit()
      • HighDimDirectLiNGAM.get_error_independence_p_values()
  • VAR-LiNGAM
    • VARLiNGAM
      • VARLiNGAM.__init__()
      • VARLiNGAM.adjacency_matrices_
      • VARLiNGAM.bootstrap()
      • VARLiNGAM.causal_order_
      • VARLiNGAM.estimate_total_effect()
      • VARLiNGAM.estimate_total_effect2()
      • VARLiNGAM.fit()
      • VARLiNGAM.get_error_independence_p_values()
      • VARLiNGAM.residuals_
  • VARMA-LiNGAM
    • VARMALiNGAM
      • VARMALiNGAM.__init__()
      • VARMALiNGAM.adjacency_matrices_
      • VARMALiNGAM.bootstrap()
      • VARMALiNGAM.causal_order_
      • VARMALiNGAM.estimate_total_effect()
      • VARMALiNGAM.estimate_total_effect2()
      • VARMALiNGAM.fit()
      • VARMALiNGAM.get_error_independence_p_values()
      • VARMALiNGAM.residuals_
  • LongitudinalLiNGAM
    • LongitudinalLiNGAM
      • LongitudinalLiNGAM.__init__()
      • LongitudinalLiNGAM.adjacency_matrices_
      • LongitudinalLiNGAM.bootstrap()
      • LongitudinalLiNGAM.causal_orders_
      • LongitudinalLiNGAM.estimate_total_effect()
      • LongitudinalLiNGAM.estimate_total_effect2()
      • LongitudinalLiNGAM.fit()
      • LongitudinalLiNGAM.get_error_independence_p_values()
      • LongitudinalLiNGAM.residuals_
  • BootstrapResult
    • BootstrapResult
      • BootstrapResult.__init__()
      • BootstrapResult.adjacency_matrices_
      • BootstrapResult.get_causal_direction_counts()
      • BootstrapResult.get_directed_acyclic_graph_counts()
      • BootstrapResult.get_paths()
      • BootstrapResult.get_probabilities()
      • BootstrapResult.get_total_causal_effects()
      • BootstrapResult.resampled_indices_
      • BootstrapResult.total_effects_
  • VARBootstrapResult
    • VARBootstrapResult
      • VARBootstrapResult.__init__()
      • VARBootstrapResult.adjacency_matrices_
      • VARBootstrapResult.get_causal_direction_counts()
      • VARBootstrapResult.get_directed_acyclic_graph_counts()
      • VARBootstrapResult.get_paths()
      • VARBootstrapResult.get_probabilities()
      • VARBootstrapResult.get_total_causal_effects()
      • VARBootstrapResult.resampled_indices_
      • VARBootstrapResult.total_effects_
  • VARMABootstrapResult
    • VARMABootstrapResult
      • VARMABootstrapResult.__init__()
      • VARMABootstrapResult.adjacency_matrices_
      • VARMABootstrapResult.get_causal_direction_counts()
      • VARMABootstrapResult.get_directed_acyclic_graph_counts()
      • VARMABootstrapResult.get_paths()
      • VARMABootstrapResult.get_probabilities()
      • VARMABootstrapResult.get_total_causal_effects()
      • VARMABootstrapResult.resampled_indices_
      • VARMABootstrapResult.total_effects_
  • LongitudinalBootstrapResult
    • LongitudinalBootstrapResult
      • LongitudinalBootstrapResult.__init__()
      • LongitudinalBootstrapResult.adjacency_matrices_
      • LongitudinalBootstrapResult.get_causal_direction_counts()
      • LongitudinalBootstrapResult.get_directed_acyclic_graph_counts()
      • LongitudinalBootstrapResult.get_paths()
      • LongitudinalBootstrapResult.get_probabilities()
      • LongitudinalBootstrapResult.get_total_causal_effects()
      • LongitudinalBootstrapResult.total_effects_
  • BottomUpParceLiNGAM
    • BottomUpParceLiNGAM
      • BottomUpParceLiNGAM.__init__()
      • BottomUpParceLiNGAM.adjacency_matrix_
      • BottomUpParceLiNGAM.bootstrap()
      • BottomUpParceLiNGAM.causal_order_
      • BottomUpParceLiNGAM.estimate_total_effect()
      • BottomUpParceLiNGAM.estimate_total_effect2()
      • BottomUpParceLiNGAM.fit()
      • BottomUpParceLiNGAM.get_error_independence_p_values()
  • RCD
    • RCD
      • RCD.__init__()
      • RCD.adjacency_matrix_
      • RCD.ancestors_list_
      • RCD.bootstrap()
      • RCD.fit()
      • RCD.get_error_independence_p_values()
  • CAM-UV
    • CAMUV
      • CAMUV.__init__()
      • CAMUV.adjacency_matrix_
      • CAMUV.fit()
  • MultiGroupRCD
    • MultiGroupRCD
      • MultiGroupRCD.__init__()
      • MultiGroupRCD.adjacency_matrices_
      • MultiGroupRCD.ancestors_list_
      • MultiGroupRCD.bootstrap()
      • MultiGroupRCD.estimate_total_effect()
      • MultiGroupRCD.estimate_total_effect2()
      • MultiGroupRCD.fit()
      • MultiGroupRCD.get_error_independence_p_values()
  • MultiGroupCAMUV
    • MultiGroupCAMUV
      • MultiGroupCAMUV.__init__()
      • MultiGroupCAMUV.adjacency_matrix_
      • MultiGroupCAMUV.fit()
  • MultiGroupRESIT
    • MultiGroupRESIT
      • MultiGroupRESIT.__init__()
      • MultiGroupRESIT.adjacency_matrices_
      • MultiGroupRESIT.bootstrap()
      • MultiGroupRESIT.causal_order_
      • MultiGroupRESIT.estimate_total_effect()
      • MultiGroupRESIT.fit()
      • MultiGroupRESIT.get_error_independence_p_values()
  • LiNA
    • LiNA
      • LiNA.__init__()
      • LiNA.adjacency_matrix_
      • LiNA.fit()
      • LiNA.measurement_matrix_
    • MDLiNA
      • MDLiNA.__init__()
      • MDLiNA.adjacency_matrix_
      • MDLiNA.fit()
      • MDLiNA.measurement_matrix_
  • RESIT
    • RESIT
      • RESIT.__init__()
      • RESIT.adjacency_matrix_
      • RESIT.bootstrap()
      • RESIT.causal_order_
      • RESIT.estimate_total_effect()
      • RESIT.fit()
      • RESIT.get_error_independence_p_values()
  • LiM
    • LiM
      • LiM.__init__()
      • LiM.adjacency_matrix_
      • LiM.fit()
  • GroupLiNGAM
    • GroupLiNGAM
      • GroupLiNGAM.__init__()
      • GroupLiNGAM.adjacency_matrix_
      • GroupLiNGAM.bootstrap()
      • GroupLiNGAM.causal_order_
      • GroupLiNGAM.fit()
  • GroupDirectLiNGAM
    • GroupDirectLiNGAM
      • GroupDirectLiNGAM.__init__()
      • GroupDirectLiNGAM.adjacency_matrix_
      • GroupDirectLiNGAM.bootstrap()
      • GroupDirectLiNGAM.causal_order_
      • GroupDirectLiNGAM.fit()
  • mLiNGAM
    • mLiNGAM
      • mLiNGAM.__init__()
      • mLiNGAM.adjacency_matrix_
      • mLiNGAM.bootstrap()
      • mLiNGAM.causal_order_
      • mLiNGAM.estimate_total_effect()
      • mLiNGAM.fit()
      • mLiNGAM.get_error_independence_p_values()
  • ABICLiNGAM
    • ABICLiNGAM
      • ABICLiNGAM.__init__()
      • ABICLiNGAM.adjacency_matrix_
      • ABICLiNGAM.bootstrap()
      • ABICLiNGAM.causal_order_
      • ABICLiNGAM.coefficient_matrix_
      • ABICLiNGAM.error_covariance_matrix_
      • ABICLiNGAM.estimate_total_effect()
      • ABICLiNGAM.fit()
      • ABICLiNGAM.get_error_independence_p_values()
  • CausalEffect
    • CausalEffect
      • CausalEffect.__init__()
      • CausalEffect.estimate_effects_on_prediction()
      • CausalEffect.estimate_optimal_intervention()
  • LEWIS
    • LEWIS
      • LEWIS.__init__()
      • LEWIS.get_scores()
  • utils
    • print_causal_directions()
    • print_dagc()
    • make_prior_knowledge()
    • remove_effect()
    • make_dot()
    • get_sink_variables()
    • get_exo_variables()
    • find_all_paths()
    • predict_adaptive_lasso()
    • likelihood_i()
    • log_p_super_gaussian()
    • variance_i()
    • extract_ancestors()
    • f_correlation()
    • visualize_nonlinear_causal_effect()
    • evaluate_model_fit()
    • calculate_distance_from_root_nodes()
    • calculate_total_effect()
    • get_common_edge_probabilities()
    • print_common_edge_directions()
    • make_dot_for_nan_probability_matrix()
    • bic_select_logistic_l1()
    • MGGD
      • MGGD.__init__()
      • MGGD.rvs()
    • MGGDEstimator
      • MGGDEstimator.__init__()
      • MGGDEstimator.fit()
  • tools
    • bootstrap_with_imputation()

Indices and tables

• Index