Causally Disentangled Generative Variational AutoEncoder

This repository is the official implementation of Causally Disentangled Generative Variational AutoEncoder (26th European Conference on Artificial Intelligence ECAI 2023 accepted paper) with pytorch.

NOTE: This repository supports WandB MLOps platform!

Appendix

See appendix.pdf for the appendix file for the main manuscript.

Training & Evaluation

1. How to Training & Evaluation

1. pendulum datset

training

• training CDG-VAE

python main.py --model "CDGVAE"

• training CDG-VAE in order to evaluate distributional robustness of downstream task

python DR/main.py --model "CDGVAE"

(Note) The file names with _semi means training CDG-VAE under semi-supervised learning setting.

evaluation

• counterfactual image generation: inference.py
• sample efficiency of downstream task: sample_efficiency.py
• distributional robustness of downstream task: DR/robustness.py

2. tabular dataset

training

• CDG-VAE

python tabular/main.py --model "CDGVAE"

• CDG-TVAE

python tabular/main_tvae.py 

evaluation (SHD and synthetic data quality)

• CDG-VAE: tabular/inference.py
• CDG-TVAE: tabular/inference_tvae.py

Results

directory and codes

.
+-- assets 
+-- modules 
|       +-- datasets.py
|       +-- model.py
|       +-- pendulum_real.py
|       +-- pendulum.py
|       +-- simulation.py
|       +-- train.py
|       +-- viz.py

+-- main.py
+-- main_semi.py
+-- main_classifier.py
+-- inference.py
+-- metric.py
+-- sample_efficiency.py
+-- LICENSE
+-- README.md
+-- appendix.pdf

+-- DR (folder which contains source codes for distributional robustness experiments)
|   +-- assets 
|   +-- main.py
|   +-- main_semi.py
|   +-- robustness.py
|   +-- toy_DR.py

+-- tabular (folder which contains source codes for tabular dataset experiments)
|   +-- assets 
|   +-- modules
|       +-- adult_datasets.py
|       +-- covtype_datasets.py
|       +-- loan_datasets.py
|       +-- errors.py
|       +-- numerical.py
|       +-- transformer_base.py
|       +-- transformer_null.py
|       +-- data_transformer.py
|       +-- model.py
|       +-- train.py
|       +-- evaluation.py
|       +-- viz.py
|       +-- simulation.py
|   +-- dag_adult.py
|   +-- dag_covtype.py
|   +-- dag_loan.py
|   +-- main.py
|   +-- inference.py
|   +-- main_tvae.py
|   +-- inference_tvae.py

Citation

@inproceedings{An2023CDGVAE,
  author       = {SeungHwan An and
                  Kyungwoo Song and
                  Jong{-}June Jeon},
  title        = {Causally Disentangled Generative Variational AutoEncoder},
  booktitle    = {{ECAI} 2023 - 26th European Conference on Artificial Intelligence,
                  September 30 - October 4, 2023, Krak{\'{o}}w, Poland - Including
                  12th Conference on Prestigious Applications of Intelligent Systems
                  {(PAIS} 2023)},
  series       = {Frontiers in Artificial Intelligence and Applications},
  volume       = {372},
  pages        = {93--100},
  publisher    = {{IOS} Press},
  year         = {2023},
  url          = {https://doi.org/10.3233/FAIA230258},
  doi          = {10.3233/FAIA230258},
}