Advances in Causal Inference With Continuous Exposures

确定因果关系是科学探究的基本目标之一。例如，疫苗能降低患病风险吗？饮用水中的铅或氨等化学物质是否对人体健康有害？因果推理是统计研究的一个领域，涉及开发使用数据来回答这些问题的方法。大多数因果推理研究都集中在二元暴露上;也就是说，暴露只能取两个值，例如治疗和控制。然而，许多感兴趣的暴露可能需要大量甚至无限数量的值，例如接受的药物或疫苗的剂量，或饮用水中物质的浓度。这些被称为“持续暴露”，在许多学科中很常见，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将开发三种方法学创新，用于连续暴露的因果推断。在前两个目标中，PI将侧重于因果剂量反应曲线，该曲线描述了因果效应如何随暴露水平而变化。为了对该曲线的形状进行有效的统计推断，PI将为因果剂量-反应曲线开发统一的置信带，并开发用于评估剂量-反应曲线参数模型拟合的工具。这些方法将允许研究人员了解连续暴露的定性影响，同时做出最小的假设。在第三个目标中，PI将解决增量转移干预效果的非参数推断，在比剂量反应曲线所需的假设更弱的假设下，提供连续暴露因果效应的有用的一个数字总结。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.