Paper | Arxiv

causal AI, biomedical informatics, generative adversarial networks, variational inference, information theory, doubly robust

The supplementary material with proofs and additional results will be found at: here.

The presentation slides will be found at:

1. PPT
2. PDF

Click this link.

All the packages are inluded in environment.yml file

• pytorch - 1.3.1
• numpy - 1.17.2
• pandas - 0.25.1
• scikit - 0.21.3
• matplotlib - 3.1.1
• python - 3.8

python 3.8

pytorch 1.3.1

First go the folder DR_Info_CFR by the command cd DR_Info_CFR and then do the following for each of the 3 datasets:

• IHDP:

Command to reproduce the experiments mentioned in the paper for IHDP dataset:

cd IHDP

python3 main_IHDP.py

• Jobs:

Command to reproduce the experiments mentioned in the paper for Jobs dataset:

cd Jobs

python3 main_Jobs.py

• Twins:

Command to reproduce the experiments mentioned in the paper for Twins dataset:

cd Twins

python3 main_Twins.py

• IHDP: IHDP/Constants.py

• Jobs: Jobs/Constants.py

• Twins: Twins/Constants.py

@inproceedings{ghosh2021dr,
  title={DR-VIDAL-Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data},
  author={Ghosh, Shantanu and Feng, Zheng and Bian, Jiang and Butler, Kevin and Prosperi, Mattia},
  booktitle={AMIA Annual Symposium Proceedings},
  volume={2022},
  pages={485},
  year={2022},
  organization={American Medical Informatics Association}
}

Licensed under the MIT License

Copyright (c) DISL, 2021