Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding

@article{Qi2021ProximalLF,
  title={Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding},
  author={Zhengling Qi and Rui Miao and Xiaoke Zhang},
  journal={ArXiv},
  year={2021},
  volume={abs/2105.01187}
}
Data-driven individualized decision making has recently received increasing research interests. Most existing methods rely on the assumption of no unmeasured confounding, which unfortunately cannot be ensured in practice especially in observational studies. Motivated by the recent proposed proximal causal inference, we develop several proximal learning approaches to estimating optimal individualized treatment regimes (ITRs) in the presence of unmeasured confounding. In particular, we establish… 

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