  Purpose of Review: Negative controls are a powerful tool to detect and adjust
for bias in epidemiological research. This paper introduces negative controls
to a broader audience and provides guidance on principled design and causal
analysis based on a formal negative control framework.
  Recent Findings: We review and summarize causal and statistical assumptions,
practical strategies, and validation criteria that can be combined with subject
matter knowledge to perform negative control analyses. We also review existing
statistical methodologies for detection, reduction, and correction of
confounding bias, and briefly discuss recent advances towards nonparametric
identification of causal effects in a double negative control design.
  Summary: There is great potential for valid and accurate causal inference
leveraging contemporary healthcare data in which negative controls are
routinely available. Design and analysis of observational data leveraging
negative controls is an area of growing interest in health and social sciences.
Despite these developments, further effort is needed to disseminate these novel
methods to ensure they are adopted by practicing epidemiologists.
