Uniform inference on continuous treatment effects via artificial neural networks in digital health

这一研究项目将提供一个通用的公式和一个深度学习驱动的工具箱，用于在观察性研究中对持续治疗效果进行因果分析。数字健康创新为收集大规模观测数据铺平了道路。在实践中，许多在医疗保健计划中的经验性应用涉及持续治疗。该项目满足了从业者的直接需求，他们寻求灵活而强大的统计工具，以基于大量和不同种类的数字健康数据对持续治疗效果进行因果推断。学生，特别是来自代表人数不足的群体的学生，将被招募参与研究。将开发易于实施的软件包并向公众提供。研究结果将为科学家和医疗保健提供者提供原则性分析，以提出治疗建议，从而改善患者护理并降低成本。配备可靠的深度学习工具箱的先进数字技术将彻底改变医疗保健分析。这项研究还将促进与医学、公共卫生、工程学和社会科学的科学家的合作。此外，该项目将为研究生提供研究培训。该项目将开发新的统计方法和相关理论，通过深度学习对持续治疗效果进行统一的因果推断。它将追求三个具体的研究主题，所开发的方法将用于解决广泛的因果问题。具体地说，在第一个主题中，该项目将开发各种神经网络架构，以近似滋扰函数，以适合数字健康中的数据应用。在第二个主题中，本项目将通过广义最优化利用神经网络来估计平衡权重，并为剂量-效应曲线构建同步置信带进行推理。在第三个主题中，该项目将把所提出的优化程序应用于非均质治疗效果的估计和纵向数据设置。这项研究将为使用深度神经网络评估一般持续治疗效果提供一个新的视角。它将为因果分析提供一个强大的工具，结合深度学习、直接协变量平衡和广义优化的优势。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。

This research project will provide a general formulation and a deep learning-powered toolbox for conducting causal analysis of continuous treatment effects in observational research. Digital health innovations have paved the way for the collection of large-scale observational data. In practice, many empirical applications in healthcare programs involve continuous treatments. This project meets the immediate needs of practitioners seeking flexible and powerful statistical tools for conducting causal inference of continuous treatment effects based on large and heterogeneous digital health data. Students, especially from underrepresented groups, will be recruited to participate in the research. Easy-to-implement software packages will be developed and made publicly available. The research results will equip scientists and healthcare providers with principled analysis for making treatment recommendations, so as to improve patient care and reduce costs. Advanced digital technologies powered with a reliable deep learning toolbox will revolutionize healthcare analytics. The research will also promote collaborations with scientists from Medicine, Public Health, Engineering, and Social Sciences. In addition, the project will provide research training for graduate students.This project will develop new statistical methodologies and the associated theories for conducting uniform causal inference of continuous treatment effects via deep learning. It will pursue three specific research topics, and the developed methods will be used to solve a wide range of causal problems. Specifically, in the first topic, the project will develop a variety of neural network architectures to approximate the nuisance function for suitable data applications in digital health. In the second topic, the project will estimate the balancing weight using neural networks through generalized optimization, and construct simultaneous confidence bands for the dose-response curve for inference. In the third topic, the project will apply the proposed optimization procedure to the estimation of heterogeneous treatment effects, and to the longitudinal data setting. The research will provide a new perspective on estimating general continuous treatment effects using deep neural networks. It will provide a powerful tool for causal analysis that combines the advantages of deep learning, direct covariate balancing, and generalized optimization.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.