  Negative control variables are increasingly used to adjust for unmeasured
confounding bias in causal inference using observational data. They are
typically identified by subject matter knowledge and there is currently a
severe lack of data-driven methods to find negative controls. In this paper, we
present a statistical test for discovering negative controls of a special type
-- disconnected negative controls -- that can serve as surrogates of the
unmeasured confounder, and we incorporate that test into the Data-driven
Automated Negative Control Estimation (DANCE) algorithm. DANCE first uses the
new validation test to identify subsets of a set of candidate negative control
variables that satisfy the assumptions of disconnected negative controls. It
then applies a negative control method to each pair of these validated negative
control variables, and aggregates the output to produce an unbiased point
estimate and confidence interval for a causal effect in the presence of
unmeasured confounding. We (1) prove the correctness of this validation test,
and thus of DANCE; (2) demonstrate via simulation experiments that DANCE
outperforms both naive analysis ignoring unmeasured confounding and negative
control method with randomly selected candidate negative controls; and (3)
demonstrate the effectiveness of DANCE on a challenging real-world problem.
