  We present new results for nonparametric identification of causal effects
using noisy proxies for unobserved confounders. Our approach builds on the
results of \citet{Hu2008} who tackle the problem of general measurement error.
We call this the `triple proxy' approach because it requires three proxies that
are jointly independent conditional on unobservables. We consider three
different choices for the third proxy: it may be an outcome, a vector of
treatments, or a collection of auxiliary variables. We compare to an
alternative identification strategy introduced by \citet{Miao2018a} in which
causal effects are identified using two conditionally independent proxies. We
refer to this as the `double proxy' approach. The triple proxy approach
identifies objects that are not identified by the double proxy approach,
including some that capture the variation in average treatment effects between
strata of the unobservables. Moreover, the conditional independence assumptions
in the double and triple proxy approaches are non-nested.
