通过表征学习解决孟德尔随机化中的仪器-结果混杂
Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
AI总结：
本文提出了一种基于表征学习的方法，通过利用跨环境不变性来解决孟德尔随机化中的仪器-结果混杂问题，并通过实验验证了其有效性。
摘要：
孟德尔随机化（MR）是一种重要的观察性流行病学研究方法，旨在估计因果效应时解决未观察到的混杂因素。然而，核心假设——特别是仪器与未观察到的混杂因素之间的独立性——由于人口分层或 assortative mating 而经常被违反。利用多环境数据日益丰富的可用性，我们提出了一种表征学习框架，利用跨环境不变性来恢复遗传仪器的潜在外生成分。我们为在各种混合机制下识别这些潜在仪器提供了理论保证，并通过模拟和使用来自 All of Us 研究枢纽的半合成实验数据展示了我们方法的有效性。
英文摘要：
Mendelian Randomization (MR) is a prominent observational epidemiological research method designed to address unobserved confounding when estimating causal effects. However, core assumptions -- particularly the independence between instruments and unobserved confounders -- are often violated due to population stratification or assortative mating. Leveraging the increasing availability of multi-environment data, we propose a representation learning framework that exploits cross-environment invariance to recover latent exogenous components of genetic instruments. We provide theoretical guarantees for identifying these latent instruments under various mixing mechanisms and demonstrate the effectiveness of our approach through simulations and semi-synthetic experiments using data from the All of Us Research Hub.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
