Proximal Balancing for Causal Effect Estimation under Unmeasured Confounding

Estimating causal effects from observational data is central to science and policy, but the effects are not identified when confounders are unmeasured. Proximal causal inference addresses this problem with proxies of the unmeasured confounders.