Mendelian randomization (MR) considers using genetic variants as instrumental variables (IVs) to infer causal effects in observational studies. However, the validity of causal inference in MR can be compromised when the IVs are potentially invalid. In this work, we propose a new method, MR-Local, to infer the causal effect in the existence of possibly invalid IVs. By leveraging the distribution of ratio estimates around the true causal effect, MR-Local selects the cluster of ratio estimates with the least uncertainty and performs causal inference within it. We establish the asymptotic normality of our estimator in the two-sample summary-data setting under either the plurality rule or the balanced pleiotropy assumption. Extensive simulations and analyses of real datasets demonstrate the reliability of our approach.

翻译：孟德尔随机化（MR）考虑使用遗传变异作为工具变量（IVs）在观察性研究中推断因果效应。然而，当工具变量可能无效时，MR中因果推断的有效性可能会受到影响。本文提出了一种新方法——MR-Local，用于在存在可能无效的工具变量时推断因果效应。通过利用比估计值在真实因果效应周围的分布，MR-Local选择不确定性最小的比估计值簇，并在其内部进行因果推断。我们在两种样本汇总数据设置下，在多数规则或平衡多效性假设下，证明了估计量的渐近正态性。广泛的模拟实验和真实数据集分析验证了我们方法的可靠性。