Negative Control Outcome Design

The negative control design uses a deliberately chosen outcome (or exposure) that cannot plausibly be caused by the exposure under study, yet is subject to the same unmeasured confounding, selection, or measurement processes as the real research question. If the exposure appears to 'affect' something it cannot possibly affect, that spurious association is a signature of residual bias. Lipsitch, Tchetgen Tchetgen, and Cohen formalized this falsification logic for epidemiology in 2010, specifying the conditions a valid negative control must satisfy. Shi, Miao, and Tchetgen Tchetgen's 2020 review extended the idea from detection toward correction, showing how pairs of negative control variables underpin proximal causal inference, which can recover an unbiased effect estimate even when the confounder is never measured.