Semiparametric efficient G-estimation with invalid instrumental variables

• 成果类型: Article
• 署名作者: Sun, B.; Liu, Z.; Tchetgen, E. J. Tchetgen
• 署名单位: National University of Singapore; Columbia University; University of Pennsylvania
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asad011
• 发表日期: 2023
• 关键词: mendelian randomization; generalized-method; CONSISTENT ESTIMATION; Multiple Robustness; Causal Inference; regression; identification; models; selection; moments

摘要

The instrumental variable method is widely used in the health and social sciences for identification and estimation of causal effects in the presence of potential unmeasured confounding. To improve efficiency, multiple instruments are routinely used, raising concerns about bias due to possible violation of the instrumental variable assumptions. To address such concerns, we introduce a new class of G-estimators that are guaranteed to remain consistent and asymptotically normal for the causal effect of interest provided that a set of at least ? out of K candidate instruments are valid, for ? = K set by the analyst ex ante without necessarily knowing the identities of the valid and invalid instruments. We provide formal semiparametric efficiency theory supporting our results. Simulation studies and applications to UK Biobank data demonstrate the superior empirical performance of the proposed estimators compared with competing methods.