ProxITR

Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding

Requirements

python >= 3.8
numpy >= 1.20
scipy >= 1.6.2
pandas >= 1.2.3
scikit-learn >= 0.24.1
pytorch >= 1.8.1

Conda Installation

conda install pandas scikit-learn numpy scipy pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c conda-forge

Content

• notebooks: Usage examples for all simulation settings in the paper, in each example:
  • samp_size: sample size is 2000 (users can try other settings)
  • qtl: the quantile for dPESS selection, default is 0.4 (users can try other settings)
• data contains file to generate simulated data
• src source files:
  • proxITR.py: main file of proximal ITR learning
  • rkhs_scaler.py: estimators of ourcome bridge function h0 and treatment bridge function q0
  • torchSVC.py: optimizer of weighted binary support vector classification

Citation

@article{qi2022proximal,
  title={Proximal learning for individualized treatment regimes under unmeasured confounding},
  author={Qi, Zhengling and Miao, Rui and Zhang, Xiaoke},
  journal={Journal of the American Statistical Association},
  number={just-accepted},
  pages={1--33},
  year={2022},
  publisher={Taylor \& Francis}
}