Using negative controls to identify causal effects with invalid instrumental variables

• 成果类型: Article
• 署名作者: Dukes, O.; Richardson, D. B.; Shahn, Z.; Robins, J. M.; Tchetgen, E. J. Tchetgen
• 署名单位: Ghent University; University of California System; University of California Irvine; City University of New York (CUNY) System; Harvard University; Harvard T.H. Chan School of Public Health; University of Pennsylvania
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asae064
• 发表日期: 2025
• 关键词: robust estimation

摘要

Many proposals for the identification of causal effects require an instrumental variable that satisfies strong, untestable unconfoundedness and exclusion restriction assumptions. In this paper, we show how one can potentially identify causal effects under violations of these assumptions by harnessing a negative control population or outcome. This strategy allows one to leverage subpopulations for whom the exposure is degenerate, and requires that the instrument-outcome association satisfies a certain parallel trend condition. We develop semiparametric efficiency theory for a general instrumental variable model, and obtain a multiply robust, locally efficient estimator of the average treatment effect in the treated. The utility of the estimators is demonstrated in simulation studies and an analysis of the Life Span Study.