FCI

Algorithm Introduction

Causal Discovery with Fast Causal Inference (FCI [1]).

Usage

from causallearn.search.ConstraintBased.FCI import fci

# default parameters
G, edges = fci(data)

# or customized parameters
G, edges = fci(data, independence_test_method, alpha, depth, max_path_length,
    verbose, background_knowledge, cache_variables_map)

# visualization
from causallearn.utils.GraphUtils import GraphUtils

pdy = GraphUtils.to_pydot(G)
pdy.write_png('simple_test.png')

Visualization using pydot is recommended. If specific label names are needed, please refer to this usage example.

Parameters

dataset: numpy.ndarray, shape (n_samples, n_features). Data, where n_samples is the number of samples and n_features is the number of features.

independence_test_method: Independence test method function. Default: ‘fisherz’.

  • “fisherz”: Fisher’s Z conditional independence test.

  • “chisq”: Chi-squared conditional independence test.

  • “gsq”: G-squared conditional independence test.

  • “kci”: kernel-based conditional independence test. (As a kernel method, its complexity is cubic in the sample size, so it might be slow if the same size is not small.)

  • “mv_fisherz”: Missing-value Fisher’s Z conditional independence test.

alpha: Significance level of individual partial correlation tests. Default: 0.05.

depth: The depth for the fast adjacency search, or -1 if unlimited. Default: -1.

max_path_length: the maximum length of any discriminating path, or -1 if unlimited. Default: -1.

verbose: True is verbose output should be printed or logged. Default: False.

background_knowledge: class BackgroundKnowledge. Add prior edges according to assigned causal connections. Default: None. For detailed usage, please kindly refer to its usage example.

cache_variables_map: This variable a map which contains the variables relate with cache. If it is not None, it should contain ‘data_hash_key’ 、’ci_test_hash_key’ and ‘cardinalities’. Default: None.

show_progress: True iff the algorithm progress should be show in console. Default: True.

Returns

G: a CausalGraph object, where G is a PAG and the illustration of its end nodes is as follows:

../../_images/pag.png
edges: list. Contains graph’s edges properties.

  • If edge.properties have the Property ‘nl’, then there is no latent confounder. Otherwise, there are possibly latent confounders.

  • If edge.properties have the Property ‘dd’, then it is definitely direct. Otherwise, it is possibly direct.

  • If edge.properties have the Property ‘pl’, then there are possibly latent confounders. Otherwise, there is no latent confounder.

  • If edge.properties have the Property ‘pd’, then it is possibly direct. Otherwise, it is definitely direct.