Multiply robust causal inference with double-negative control adjustment for categorical unmeasured confounding

• Published in: Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 82; no. 2; pp. 521 - 540
• Main Authors: Shi, Xu, Miao, Wang, Nelson, Jennifer C., Tchetgen, Eric J. Tchetgen
• Format: Journal Article
• Language: English
• Published: England Wiley 01.04.2020; Oxford University Press
• Subjects: Bias; Causal inference; children; Computer simulation; Epidemiology; equations; Inference; Licensing; monitoring; Negative control; Observational studies; Original Articles; Parameter estimation; Popularity; Regression analysis; Robustness; Semiparametric inference; Simulation; Statistical methods; Statistics; Surveillance; Unmeasured confounding; vaccines; Causal inference; Semiparametric inference; Unmeasured confounding; Negative control

Unmeasured confounding is a threat to causal inference in observational studies. In recent years, the use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a long-standing tradition in laboratory sciences and epidemiology...