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