![]() Biologists employ “negative controls” as a means of ruling out possible noncausal interpretations of their results. Nonetheless, experimental biologists routinely question whether they have correctly inferred causal relationships from the results of their experiments. In experimental biology, the manipulation of experimental conditions prevents many of the noncausal associations that arise in observational studies. The dashed line between L and U indicates that either may cause the other, and they may share common causes. We conclude that negative controls should be more commonly employed in observational studies, and that additional work is needed to specify the conditions under which negative controls will be sensitive detectors of other sources of error in observational studies.Ĭausal diagram for the effect of an exposure of interest (A) on an outcome of interest (Y), with confounders L (assumed measured) and U (assumed uncontrolled) that cause both A and Y. We distinguish two types of negative controls (exposure controls and outcome controls), describe examples of each type from the epidemiologic literature, and identify the conditions for the use of such negative controls to detect confounding. In epidemiology, analogous negative controls help to identify and resolve confounding as well as other sources of error, including recall bias or analytic flaws. We argue, however, that a routine precaution taken in the design of biological laboratory experiments-the use of “negative controls”-is designed to detect both suspected and unsuspected sources of spurious causal inference. Such problems are not expected to compromise experimental studies, where careful standardization of conditions (for laboratory work) and randomization (for population studies) should, if applied properly, eliminate most such non-causal associations. Many techniques have been developed for study design and analysis to identify and eliminate such errors. Non-causal associations between exposures and outcomes are a threat to validity of causal inference in observational studies.
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