  We consider a causal effect that is confounded by an unobserved variable, but
with observed proxy variables of the confounder. We show that, with at least
two independent proxy variables satisfying a certain rank condition, the causal
effect is nonparametrically identified, even if the measurement error
mechanism, i.e., the conditional distribution of the proxies given the con-
founder, may not be identified. Our result generalizes the identification
strategy of Kuroki & Pearl (2014) that rests on identification of the
measurement error mechanism. When only one proxy for the confounder is
available, or the required rank condition is not met, we develop a strategy to
test the null hypothesis of no causal effect.
