论文标题
用噪音打击噪音：与许多候选工具的因果推断
Fighting Noise with Noise: Causal Inference with Many Candidate Instruments
论文作者
论文摘要
仪器变量方法提供了有用的工具，可以在存在未衡量的混淆的情况下推断因果效应。要使用大规模数据集应用这些方法，一个主要的挑战是从可能的大型候选人组中找到有效的工具。实际上，大多数候选工具通常与研究特定的关注曝光无关。此外，并非所有相关候选工具都是有效的，因为它们可能直接影响感兴趣的结果。在本文中，我们提出了一种数据驱动的方法，用于与许多候选工具同时解决这两个挑战的方法。我们提案的一个关键组成部分涉及使用伪变量（已知是无关紧要的）来从原始集合中删除与暴露的虚假相关性的变量。合成数据分析表明，与现有方法相比，所提出的方法的性能优惠。我们将方法应用于孟德尔随机研究，估计肥胖对健康相关生活质量的影响。
Instrumental variable methods provide useful tools for inferring causal effects in the presence of unmeasured confounding. To apply these methods with large-scale data sets, a major challenge is to find valid instruments from a possibly large candidate set. In practice, most of the candidate instruments are often not relevant for studying a particular exposure of interest. Moreover, not all relevant candidate instruments are valid as they may directly influence the outcome of interest. In this article, we propose a data-driven method for causal inference with many candidate instruments that addresses these two challenges simultaneously. A key component of our proposal involves using pseudo variables, known to be irrelevant, to remove variables from the original set that exhibit spurious correlations with the exposure. Synthetic data analyses show that the proposed method performs favourably compared to existing methods. We apply our method to a Mendelian randomization study estimating the effect of obesity on health-related quality of life.
