Flexible Instrumental Variable Models With Bayesian Additive Regression Trees
基于贝叶斯加性回归树的灵活工具变量模型AI 解读 ↓
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基于贝叶斯加性回归树的灵活工具变量模型
- 研究问题
- 存在未观测混杂时，如何利用工具变量估计包含非线性和交互作用的关系，减轻传统线性与可加性模型的限制？
- 方法与思路
- 作者以贝叶斯回归树集成构建非线性工具变量回归，并在 UK Biobank 中用遗传变异作工具，分析体重指数与血压关系。
- 材料陈述的贡献
- 摘要提出能够表示非线性及交互项的贝叶斯工具变量方法，并以孟德尔随机化应用展示其对个体异质性问题的适用性。
- 阅读关注点
- 应核对工具变量有效性、非线性模型的识别依据、树模型与先验设定，以及年龄交互和后验不确定性如何解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审与接收未核实。摘要未说明完整识别条件或比较结果，模型灵活性不能替代工具变量有效性。
原文摘要
Methods utilizing instrumental variables have been a fundamental statistical approach to estimation in the presence of unmeasured confounding, usually occurring in non-randomized observational data common to fields such as economics and public health. However, such methods usually make constricting linearity and additivity assumptions that are inapplicable to the complex modeling challenges of today. The growing body of observational data being collected will necessitate flexible regression modeling while also being able to control for confounding using instrumental variables. Therefore, this article presents a nonlinear instrumental variable regression model based on Bayesian regression tree ensembles to estimate such relationships, including interactions, in the presence of confounding. One exciting application of this method is to use genetic variants as instruments, known as Mendelian randomization. Body mass index is one factor that is hypothesized to have a nonlinear relationship with cardiovascular risk factors such as blood pressure while interacting with age. Heterogeneity in patient characteristics such as age could be clinically interesting from a precision medicine perspective where individualized treatment is emphasized. We present our flexible Bayesian instrumental variable regression tree method with an example from the UK Biobank where body mass index is related to blood pressure using genetic variants as the instruments.