arXiv stat.ML
· Papers
Partial Identification of Causal Effects Using Proxy Variables
arXiv:2304.04374v4 Announce Type: replace-cross Abstract: Proximal causal inference is a framework for evaluating the causal effects in the presence of unmeasured confounding. For point identification, it leverages a pair of proxy variables to identify a bridge function that matches the dependence of potential outcomes