Advances in Causal Inference With Continuous Exposures

连续暴露因果推理的进展

• 批准号: 2113171
• 负责人: Theodore Westling
• 金额: $ 17.78万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113171&HistoricalAwards=false
• 关键词: Advances; Causal; Inference; Continuous; Exposures

Determining cause and effect is one of the fundamental goals of scientific inquiry. For instance, does a vaccine reduce risk of disease? Is a chemical such as lead or ammonia in drinking water harmful to human health? Causal inference is the area of statistical research concerned with developing methods for using data to answer such questions. The majority of causal inference research has focused on binary exposures; that is, exposures that can only take two values, such as treatment and control. However, many exposures of interest can take a large or even infinite number of values, such as the dose of a drug or vaccine received, or the concentration of a substance in drinking water. These are called "continuous exposures", and are commonplace in many disciplines, including biomedicine, epidemiology, public health, and economics. In this project, the PI will develop flexible statistical methods for assessing the causal effects of continuous exposures.The PI will develop three methodological innovations for causal inference with continuous exposures. In the first two aims, the PI will focus on the causal dose-response curve, which describes how the causal effect changes as a function of the exposure level. In order to make valid statistical inference regarding the shape of this curve, the PI will develop a uniform confidence band for the causal dose-response curve and develop tools for assessing the fit of parametric models for the dose-response curve. These methods will permit researchers to understand the qualitative effect of a continuous exposure while making minimal assumptions. In the third aim, the PI will address nonparametric inference on the effect of incremental shift interventions, which provide useful one-number summaries of the causal effect of continuous exposures under weaker assumptions than those necessary for the dose-response curve. User-friendly software implementing the methods developed in each of these aims will be made freely available.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

确定因果关系是科学探究的基本目标之一。例如，疫苗能降低疾病风险吗？饮用水中的铅或氨等化学物质对人体健康有害吗？因果推断是统计学研究的领域，它涉及开发使用数据来回答这些问题的方法。大多数因果推断研究都集中在二元暴露，即只能取两个值的暴露，如治疗和控制。然而，许多感兴趣的接触可能会有很大甚至无限多的值，例如接受的药物或疫苗的剂量，或饮用水中某种物质的浓度。这种情况被称为“持续暴露”，在许多学科中都很常见，包括生物医学、流行病学、公共卫生和经济学。在这个项目中，PI将开发灵活的统计方法来评估连续暴露的因果影响。PI将为连续暴露的因果推断开发三种方法创新。在前两个目标中，PI将重点放在因果剂量-反应曲线上，该曲线描述了因果效应如何随暴露水平的变化而变化。为了对这条曲线的形状做出有效的统计推断，PI将为因果剂量-反应曲线制定一个统一的置信度范围，并开发工具来评估剂量-反应曲线的参数模型的适合性。这些方法将使研究人员在做出最小假设的同时，了解持续暴露的定性影响。在第三个目标中，PI将解决对增量移位干预效果的非参数推断，这将在比剂量-反应曲线所需的假设更弱的假设下，对连续暴露的因果效应提供有用的一号总结。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。