• Corpus ID: 235367652

Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation

@inproceedings{Xu2021DeepPC,
  title={Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation},
  author={Liyuan Xu and Heishiro Kanagawa and Arthur Gretton},
  booktitle={Neural Information Processing Systems},
  year={2021}
}
Proxy causal learning (PCL) is a method for estimating the causal effect of treatments on outcomes in the presence of unobserved confounding, using proxies (structured side information) for the confounder. This is achieved via two-stage regression: in the first stage, we model relations among the treatment and proxies; in the second stage, we use this model to learn the effect of treatment on the outcome, given the context provided by the proxies. PCL guarantees recovery of the true causal… 

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