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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. 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 (setting A). For convenience, we adopted the one available for download at 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.

Citation

@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}
}