  We propose a semiparametric test to evaluate (i) whether different
instruments induce subpopulations of compliers with the same observable
characteristics on average, and (ii) whether compliers have observable
characteristics that are the same as the full population on average. The test
is a flexible robustness check for the external validity of instruments. We use
it to reinterpret the difference in LATE estimates that Angrist and Evans
(1998) obtain when using different instrumental variables. To justify the test,
we characterize the doubly robust moment for Abadie (2003)'s class of complier
parameters, and we analyze a machine learning update to $\kappa$ weighting.
