  Estimating the individual treatment effect (ITE) from observational data is
essential in medicine. A central challenge in estimating the ITE is handling
confounders, which are factors that affect both an intervention and its
outcome. Most previous work relies on the unconfoundedness assumption, which
posits that all the confounders are measured in the observational data.
However, if there are unmeasurable (latent) confounders, then confounding bias
is introduced. Fortunately, noisy proxies for the latent confounders are often
available and can be used to make an unbiased estimate of the ITE. In this
paper, we develop a novel adversarial learning framework to make unbiased
estimates of the ITE using noisy proxies.
