Bayesian Nonparametric Causal Inference for High-Dimensional Nutritional Data via Factor-Based Exposure Mapping
通过因子暴露映射对高维营养数据开展贝叶斯非参数因果推断AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确以高维饮食暴露的异质性因果效应为目标，并扩展贝叶斯因果森林，直接支持因果推断分类。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
通过因子暴露映射对高维营养数据开展贝叶斯非参数因果推断
- 研究问题
- 如何将高维且相关的食物摄入转化为潜在饮食模式，并估计不同模式暴露水平对健康结果的异质性因果效应？
- 方法与思路
- 作者以因子分析构造暴露映射，扩展贝叶斯因果森林以处理三个有序暴露水平，并用模拟和美国多中心营养研究评估。
- 材料陈述的贡献
- 摘要称，方法识别出六种饮食模式并分析其与体重指数及空腹胰岛素的关系，部分模式的较高摄入与较低风险相关。
- 阅读关注点
- 应核对因子暴露映射对应的干预含义、混杂控制与重叠条件、暴露分级依据，以及模式估计误差是否进入效应不确定性。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要未列出因果识别假设或效应数值，不能把所述关联直接视为确证因果。
原文摘要
Diet plays a crucial role in health, and understanding the causal effects of dietary patterns is essential for informing public health policy and personalized nutrition strategies. However, causal inference in nutritional epidemiology faces several challenges: (i) high-dimensional and correlated food/nutrient intake data induce massive treatment levels; (ii) nutritional studies are interested in latent dietary patterns rather than single food items; and (iii) the goal is to estimate heterogeneous causal effects of these dietary patterns on health outcomes. We address these challenges by introducing a sophisticated exposure mapping framework that reduces the high-dimensional treatment space via factor analysis and enables the identification of dietary patterns. We also extend the Bayesian Causal Forest to accommodate three ordered levels of dietary exposure, better capturing the complex structure of nutritional data and enabling estimation of heterogeneous causal effects. We evaluate the proposed method through extensive simulations and apply it to a multi-center epidemiological study of Hispanic/Latino adults residing in the US. Using high-dimensional dietary data, we identify six dietary patterns and estimate their causal link with two key health risk factors: body mass index and fasting insulin levels. Our findings suggest that higher consumption of plant lipid-antioxidant, plant-based, animal protein, and dairy product patterns is associated with reduced risk.