Journal
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
Volume 82, Issue 2, Pages 521-540Publisher
WILEY
DOI: 10.1111/rssb.12361
Keywords
Causal inference; Negative control; Semiparametric inference; Unmeasured confounding
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Funding
- Beijing Natural Science Foundation, China [Z190001]
- Key Laboratory of Mathematical Economics and Quantitative Finance (Peking University), Ministry of Education, China
- Vaccine Safety Datalink from the Centers for Disease Control and Prevention [200-2012-53421]
- National Institutes of Health [R01AI104459]
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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 propose multiply 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.
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