Valid Instrumental Variables Selection Methods using Negative Control Outcomes and Constructing Efficient Estimator

TL;DR

This paper introduces a novel method for selecting valid instrumental variables in observational studies using negative control outcomes, improving causal effect estimation accuracy.

Contribution

It proposes a new strategy leveraging negative control outcomes for IV selection and a two-step estimator with proven semiparametric efficiency.

Findings

Proposed method outperforms existing estimators in simulations.

Effectively excludes invalid IVs without prior candidate information.

Achieves semiparametric efficiency in causal effect estimation.

Abstract

In observational studies, instrumental variable (IV) methods are commonly applied when there exists some unmeasured covariates. In Mendelian Randomization (MR), constructing an allele score by using many single nucleotide polymorphisms (SNPs) is often implemented; however, there are risks estimating biased causal effects by including some invalid IVs. Invalid IVs are candidates of IVs associated with some unobserved variables. To solve this problem, we propose a novel strategy in this paper: using Negative Control Outcomes (NCOs) as auxiliary variables. By using NCOs, we can essentialy select only valid IVs and exclude invalid IVs without any information of IV candidates. We also propose the new two-step estimating procedure and prove the semiparametric efficiency. We demonstrate the superior performance of the proposed estimator compared with existing estimators via simulation studies.