# CGNN

**Repository Path**: mirrors_lepy/CGNN

## Basic Information

- **Project Name**: CGNN
- **Description**: Replication code for the article "Learning Functional Causal Models with Generative Neural Networks"
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-02-24
- **Last Updated**: 2025-07-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Tensorflow Implementation of the CGNN

Code provided to reproduce the results from the article "Learning Functional Causal Models with Generative Neural Networks"

Requirements:
numpy
scipy
scikit-learn
tensorflow
joblib
pandas

### In order to run the CGNN and launch the experiments:
1) First install the CGNN package. Enter in the code directory. Run the command line "python setup.py install develop --user"

2) Launch the example python script for pairwise inference: "python run_GNN_pairwise_inference.py"

3) Launch the example python script for graph reconstruction from a skeleton: "python run_CGNN_graph.py"

4) Launch the example python script for graph reconstruction in presence of hidden variables: "python run_CGNN_graph_hidden_variables.py"

5) The complete datasets used in the article may be found at the following url:
- pairwise datasets : http://dx.doi.org/10.7910/DVN/3757KX
- graph datasets : http://dx.doi.org/10.7910/DVN/UZMB69


# Fast Pytorch implementation of CGNN available in the CDT

A faster implementation of CGNN in pytorch in available in the CausalDiscoveryToolBox (CDT)

https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox

arXiv paper of the CDT: https://arxiv.org/abs/1903.02278