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

Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls