Project 2: Causal Relationship Disentangler for Precision Nutrition

项目2：精准营养的因果关系解开器

• 批准号: 10552678
• 负责人: SAMANTHA KLEINBERG
• 金额: $ 18.72万
• 依托单位: GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-19 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552678
• 关键词: Address; Artificial Intelligence; Biometry; Chronic Disease; Computer software; Data; Data Science; Diet; Dietary Practices; Environment; Equilibrium; Face; Feedback; Food; Food Patterns; Funding; Genetic; Goals; Guidelines; Health; Individual; Informatics; Intervention; Knowledge; Learning; Link; Mental Health; Methods; Modeling; Outcome; Participant; Pathway interactions; Pattern; Persons; Physiology; Population; Precision Health; Risk Reduction; Social Characteristics; Specific qualifier value; Structure; System; Translating; United States National Institutes of Health; Work; causal model; cost; data preservation; dietary; dietary guidelines; emotional eating; improved; individual response; innovation; learning strategy; novel strategies; nutrition; physical conditioning; precision nutrition; preservation; programs; statistics; tool

Abstract-Project 2: Causal Relationship Disentangler for Precision Nutrition Predicting individual responses to food and dietary patterns, the stated goal of the National Institutes of Health (NIH) Common Fund’s Nutrition for Precision Health program, requires uncovering the causal connections between diet and health. Despite the importance of diet for treating and reducing risk of many chronic diseases, guidelines often rely on associations rather than causal relationships. Establishing a causal model (set of causal relationships) is vital to provide accurate dietary guidelines to individuals and help them balance priorities. The key obstacles to a comprehensive model of causes and effects of diet have been a lack of methods to translate findings to new populations and a lack of data suitable to learn about causes. The first major challenge is understanding to whom and under what conditions a finding applies. There are no existing methods that can identify causal relationships between diet and other factors and can determine when these findings apply. A second core obstacle is that dietary studies often capture different sets of variables due to the cost and challenge of collecting data on the many causes and effects of nutrition, and many studies rely on food logs kept by participants. This leads to missing variables and missing values, and both can confound causal inference. Many methods exist for imputing missing values but they may lead to unacceptable errors for individuals based on patterns of missingness in real-world data. Single imputation methods provide a single value for each missing instance. Thus, given the type of missingness we face in nutrition (both missing at random [MAR] and missing not at random [MNAR]) and the importance of establishing causal relationships rather than correlations, there is a significant need for new imputation methods. To address this, we introduce new approaches for handling missing data that preserve causal structure. In the Causal Relationship Disentangler for Precision Nutrition we propose new methods for causal generalizability that learn when and why causal relationships are true. Our methods are applicable to all health outcomes and timescales. Learning how to transfer causal knowledge and doing so with missing data is critically important for realizing the potential of nutrition for precision health. Precision health requires knowing what conclusions we can draw about both populations and individuals and being able to systematically predict what interventions will work for an individual. Our automated approaches to generalizing causal models will provide the critical link between data and actions, allowing the knowledge created to generalize beyond All of Us. Our investigative team has for over a decade developed new methods that learn causal models from observational data and provide automated causal explanations, as well as statistics, data science, and biostatistics. Aim 1 will develop methods for generalizing causal relationships and learning when they apply. Aim 2 will develop improved methods for reconstructing missing data that preserve causal structure. Aim 3 will develop individual and generalizable causal models of nutrition and health.

摘要-项目2：精准营养的因果关系分解器 预测个人对食物和饮食模式的反应，国家研究所的既定目标 美国国家卫生研究院（NIH）共同基金的营养精准健康计划，要求揭示因果关系， 饮食与健康之间的联系尽管饮食对于治疗和降低许多疾病的风险很重要， 对于慢性疾病，指南往往依赖于关联而不是因果关系。建立因果 模型（一组因果关系）对于为个人提供准确的饮食指南并帮助他们 平衡优先级。建立饮食因果关系综合模型的主要障碍是缺乏 缺乏将研究结果转化为新人群的方法，以及缺乏适合了解原因的数据。第一 主要的挑战是了解调查结果适用于谁以及在什么条件下适用。没有现有 这些方法可以确定饮食和其他因素之间的因果关系，并可以确定这些因素何时发生。 调查结果适用。第二个核心障碍是，饮食研究往往捕捉不同的变量集， 收集有关营养的许多原因和影响的数据的成本和挑战，许多研究依靠 参与者保存的食物日志。这会导致缺失变量和缺失值，两者都可能混淆 因果推理有许多方法可以估算缺失值，但它们可能导致不可接受的误差， 基于真实世界数据中的缺失模式。单一插补方法提供单一 每个缺失实例的值。因此，考虑到我们在营养方面面临的缺失类型（两者都缺失）， 随机（MAR）和非随机缺失（MNAR））以及确定因果关系的重要性 如果我们认为这是一种关系而不是相关性，那么就非常需要新的估算方法。到 为了解决这个问题，我们引入了新的方法来处理丢失的数据，保持因果结构。 在精准营养的因果关系分解器中，我们提出了因果关系的新方法。 学习因果关系何时以及为何为真的普遍性。我们的方法适用于 所有的健康结果和时间表。学习如何转移因果知识，并在缺失的情况下这样做 数据对于实现营养对精准健康的潜力至关重要。精准健康需要 知道我们可以从人群和个体中得出什么结论， 系统地预测哪些干预措施对个人有效。我们的自动化方法， 因果模型将提供数据和行动之间的关键联系，使创造的知识， 超越我们所有人。我们的调查团队十多年来一直在开发新的方法， 因果模型从观测数据，并提供自动因果解释，以及统计，数据 科学和生物统计学。目标1将发展概括因果关系和学习的方法， 他们适用。Aim 2将开发改进的方法来重建缺失数据， 结构目标3将发展个别和可推广的营养和健康因果模式。