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Code for "Learning End-to-End Patient Representations through Self-Supervised Covariate Balancing for Causal Treatment Effect Estimation"

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# BCAUSS
This repo provides the code for reproducing the experiments in [Learning End-to-End Patient Representations through
Self-Supervised Covariate Balancing for Causal Treatment Effect Estimation](https://doi.org/10.1016/j.jbi.2023.104339). BCAUSS is a multi-task deep neural network for causal treatment effect estimation able to achieve minimal dissimilarity in learning treated and untreated distributions, thanks to the adoption of a specific auto-balancing self-supervised objective.

### Dependency

This implementation is based on Tensorflow and Keras. We recommend to create a proper enviroment, e.g. with dependencies specified in ```env.yml``` (```env_gpu.yml``` for GPUs):

```
conda env create -f env.yml
conda activate bcauss
```

### Data

__IHDP__: the Infant Health and Development Program (IHDP) is a randomized controlled study designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. It is generated via the npci package [`https://github.com/vdorie/npci`](https://github.com/vdorie/npci) (setting A). For convenience, we adopted the one available for download at [https://www.fredjo.com/](https://www.fredjo.com/), which is composed by 1000 repetitions of the experiment, where each one contains 747 observations. We average over 1000 train/validation/test splits with ratios 70/20/10.

We recommend to store datasets into folders like ```datasets/IHDP```, e.g.

```
mkdir -p datasets/IHDP
cd datasets/IHDP

wget --no-check-certificate http://www.fredjo.com/files/ihdp_npci_1-1000.train.npz.zip
unzip ihdp_npci_1-1000.train.npz.zip
rm ihdp_npci_1-1000.train.npz.zip

wget --no-check-certificate http://www.fredjo.com/files/ihdp_npci_1-1000.test.npz.zip
unzip ihdp_npci_1-1000.test.npz.zip
rm ihdp_npci_1-1000.test.npz.zip
```

### Running paper experiments 
We aim to use common configuration instead of tuning separately for all the settings. Hence, unless otherwise specified (e.g. ablation study), experiments adopt learning rate 1e-5, ReLU activation function, batch size equal to the train set length and stochastic gradient descent with momentum (0.9), auto-balancing objective with the same importance as the regression objective. For example, to reproduce the results of BCAUSS on IHDP

$ python train.py \
        --data_base_dir datasets/IHDP\
        --knob bcauss\
        --output_base_dir result/ihdp_csv_1-1000\
        --b_ratio 1.0\
        --bs_ratio 1.0\
        --act_fn relu\
        --optim sgd\
        --lr 1e-5\
        --momentum 0.9\
        --val_split 0.22\
        
To reproduce the different combinations of the tables and to reproduce the ablation study, use the options

* ```--use_bce``` to adopt the binary-cross-entropy objective, 
* ```--use_targ_term``` to adopt the targeted normalization term, 
* ```--optim``` to adopt different optimizers (e.g. adam), 
* ```--act_fn``` to adopt different activation functions (e.g. elu), 
* ```--bs_ratio``` to adopt different batch size ratios (1.0=all trainset), 
* ```--b_ratio``` to adopt different importance of the auto-balancing objective, 
* ```--lr``` to adopt different learning rates, 
* ```--momentum``` to adopt different momentum, 
* ```--val_split``` to adopt different x-val split ratios.

To evaluate model performance of experiments, use ```evaluate.py```, e.g.

$ python evaluate.py \
        --data_base_dir ihdp_csv_1-1000

### Treated and untreated distributions induced by the learned representation

To see the treated and untreated distributions induced by the learned representation on a sample experiment, see this notebook [Learned_Representations.ipynb](Learned_Representations.ipynb).

## Citation

```bibtex
@article{tesei2023learning,
  title={Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation},
  author={Tesei, Gino and Giampanis, Stefanos and Shi, Jingpu and Norgeot, Beau},
  journal={Journal of Biomedical Informatics},
  volume={140},
  pages={104339},
  year={2023},
  publisher={Elsevier}
}

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