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
• ISSN: 1369-7412; 1467-9868
• DOI: 10.1111/rssb.12361

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 to rule out non-causal explanations, although they have been used primarily for bias detection. Recently, Miao and colleagues have described sufficient conditions under which a pair of negative control exposure and outcome variables can be used to identify non-parametrically the average treatment effect (ATE) from observational data subject to uncontrolled confounding. We establish non-parametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables.We also provide a general semiparametric framework for obtaining inferences about the ATE while leveraging information about a possibly large number of measured covariates. In particular, we derive the semiparametric efficiency bound in the non-parametric model, and we proposemultiply robust and locally efficient estimators when non-parametric estimation may not be feasible.We assess the finite sample performance of our methods in extensive simulation studies. Finally, we illustrate our methods with an application to the post-licensure surveillance of vaccine safety among children.