LASSO-type instrumental variable selection methods with an application to Mendelian randomization.

ABSTRACT: Valid instrumental variables (IVs) must not directly impact the outcome variable and must also be uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid. The existing methods can lead to large bias relative to standard errors in situations with many weak and invalid instruments. In this paper, we derive a LASSO procedure for the k-class IV estimation methods in the linear IV model. In addition, we propose the jackknife IV method by using LASSO to address the problem of many weak invalid instruments in the case of heteroscedastic data. The proposed methods are robust for estimating causal effects in the presence of many invalid and valid instruments, with theoretical assurances of their execution. In addition, two-step numerical algorithms are dev

SUBMITTER: Qasim M 

PROVIDER: S-EPMC11874601 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature