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

ABSTRACT: 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

SUBMITTER: Shi X 

PROVIDER: S-EPMC7768794 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature