# FCI

## Algorithm Introduction

Causal Discovery with Fast Causal Inference (FCI 2).

## Usage

    from causal-learn.search.ConstraintBased.FCI import fci
    G = fci(data, indep_test, alpha, verbose=True)

## Parameters

data: Input data matrix

alpha: Significance level of individual partial correlation tests.

indep_test: Independence test method function.

  - “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.

verbose: 0 - no output, 1 - detailed output.

## Returns

cg : a CausalGraph object.

2

  Spirtes, P., Meek, C., & Richardson, T. (1995, August). Causal inference in the presence of latent variables and selection bias. In Proceedings of the Eleventh conference on Uncertainty in artificial intelligence (pp. 499-506).