On the instrumental variable estimation with many weak and invalid instruments

• 成果类型: Article
• 署名作者: Lin, Yiqi; Windmeijer, Frank; Song, Xinyuan; Fan, Qingliang
• 署名单位: Chinese University of Hong Kong; University of Oxford; University of Oxford; Chinese University of Hong Kong; Chinese University of Hong Kong
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkae025
• 发表日期: 2024
• 页码: 1068-1088
• 关键词: mendelian randomization; Lasso; regression; selection

摘要

We discuss the fundamental issue of identification in linear instrumental variable (IV) models with unknown IV validity. With the assumption of the 'sparsest rule', which is equivalent to the plurality rule but becomes operational in computation algorithms, we investigate and prove the advantages of non-convex penalized approaches over other IV estimators based on two-step selections, in terms of selection consistency and accommodation for individually weak IVs. Furthermore, we propose a surrogate sparsest penalty that aligns with the identification condition and provides oracle sparse structure simultaneously. Desirable theoretical properties are derived for the proposed estimator with weaker IV strength conditions compared to the previous literature. Finite sample properties are demonstrated using simulations and the selection and estimation method is applied to an empirical study concerning the effect of body mass index on diastolic blood pressure.