CYP0630 / CausalTGAN-1

Causal-TGAN

This is the code for the project Causal-TGAN: Causally-Aware Tabular Data Generative Adversarial Networks

Citation

If you use this code for your research, please cite:

@inproceedings{
wen2022causaltgan,
title={Causal-{TGAN}: Modeling Tabular Data Using Causally-Aware {GAN}},
author={Bingyang Wen and Yupeng Cao and Fan Yang and Koduvayur Subbalakshmi and Rajarathnam Chandramouli},
booktitle={ICLR Workshop on Deep Generative Models for Highly Structured Data},
year={2022},
url={https://openreview.net/forum?id=BEhxCh4dvW5}
}

Prerequisties

The project is built on python3.6 with the pytorch version of 1.9.0.

To run this project, please add a new python environment path to this project. For example if your cloned repository reside in /home/username/CausalTGAN, then one way to do this is export PYTHONPATH="/home/username" from command line or add it to your ~/.bashrc.

To prepare a dataset for training Causal-TGAN

• An example dataset is illustrated in ./data/real_world/adult.
  

• Instructions of adding the customized dataset is following:

  • Step 1: Add your dataset name and name of column into helper/constant.py
  • Step 2: Identify your data type (Continuous, Discrete, Mix) and

To train a Causal-TGAN

python train.py. The training details such as dataset and epochs are set inside train.py

To sample from Causal-TGAN

python sampling.py. The details such as number of samples to generate and causal-TGAN path are set inside sampling.py