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多伦多大学孔德含博士:Fighting Noise with Noise: Causal Inference with Many Candidate Instruments

光华讲坛——社会名流与企业家论坛第 6182 期

Fighting Noise with Noise: Causal Inference with Many Candidate Instruments

主讲人多伦多大学孔德含博士

主持人统计学院林华珍教授

时间2022622日(周三)上午10:30-11:30

直播平台及会议ID:腾讯会议,ID: 281-493-147

主办单位:统计研究中心和统计学院 科研处

主讲人简介:

孔德含是多伦多大学统计学助理教授,研究方向包括脑图像,函数型数据分析,因果推断,高维数据分析以及机器学习。现任美国统计学会会刊副主编。

内容提要:

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 is a novel resampling method, which constructs pseudo variables to remove irrelevant candidate instruments having 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.


工具变量方法为在存在无法测量的混杂因素的情况下推断因果效应提供了有用的工具。要将这些方法应用于大规模数据集,一个主要挑战是从可能很大的候选数据集中找到有效的工具。在实践中,大多数的候选工具往往与研究特定的研究兴趣敞口无关。此外,并非所有相关的候选工具都是有效的,因为它们可能直接影响研究兴趣的结果。本报告中,主讲人团队提出了一种数据驱动的因果推断方法,该方法包含许多候选工具,可以同时解决这两个挑战。他们提议的一个关键组成部分是一种新颖的重采样方法,该方法构造伪变量以删除与曝光具有虚假相关性的不相关候选工具。综合数据分析表明,与现有方法相比,该方法具有良好的性能。他们将此方法应用于孟德尔随机化研究,评估肥胖对健康相关生活质量的影响。


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