Official implementation of DR-VIDAL - Doubly Robust Variational Information(Theoretic) Adversarial Learning for the Estimation of Treatment Effects and Counterfactuals (AMIA, Symoissium, 2022)
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:
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
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