Robust Post-Selection Inference with Application to Subgroup Analysis

稳健的选择后推理及其在子组分析中的应用

基本信息

批准号：
2015325
负责人：
Jingshen Wang
金额：
$ 22万
依托单位：
University of California-Berkeley
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015325&HistoricalAwards=false
关键词：
Robust Post Selection Inference Application

项目摘要

Researchers have been facing challenges from high-dimensional data that contain many different characteristics for each subject. For example, biomedical scientists analyze tens of thousands of genomes to determine the cause of disease and find the most promising treatments; social scientists study differential policy impacts by leveraging vast amounts of personal data gathered from social media. This project lays out two lines of research aimed at developing valid statistical inference in this challenging environment. The insights and tools developed from this project would contribute to the advancement of a wide variety of other disciplines, including economics, education, health care, and biomedical sciences. In addition, graduate students will be engaged in the project to study the statistical guarantees and to develop relevant software packages for the research. Since the project develops modern statistical methodologies with substantial applications, it will be suitable for training graduate students with a broad range of skills.This project addresses two research problems and seeks to provide insights, theory and tools for more informed decision making in high dimensions. The first problem focuses on studying the treatment effect heterogeneity. Quantification and characterization of heterogeneous treatment effects play an increasingly important role in evaluating the efficacy of social programs and medical treatments in the presence of high-dimensional covariates. In particular, the research will develop efficient procedures for estimating heterogeneous quantile treatment effects and subgroup average treatment effects via covariate balancing. The second problem focuses on studying the validity and invalidity of data splitting for conducting inference with high-dimensional data. The project will address the issue of “random-splitting bias” in estimating the regression coefficients when the number of dummy/imbalanced variables is sizable. To overcome this challenge, the project will develop a guided data splitting framework that splits the data into more balanced halves. Because the usage of random data splitting goes beyond post-selection inference, the framework developed and the possible solutions derived from it are broadly applicable to many data-driven investigations.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.

研究人员一直面临着高维数据的挑战，这些数据包含每个主题的许多不同特征。例如，生物医学科学家分析成千上万的基因组来确定疾病的原因并找到最有前途的治疗方法。社会科学家通过利用从社交媒体收集的大量个人数据来研究差异政策的影响。该项目提出了两项旨在在此挑战环境中开发有效统计推断的研究线。该项目开发的见解和工具将有助于其他各种学科的发展，包括经济学，教育，医疗保健和生物医学科学。此外，研究生将参与该项目，以研究统计保证并为研究开发相关的软件包。由于该项目开发了具有大量应用的现代统计方法，因此它适用于具有广泛技能的培训研究生。该项目解决了两个研究问题，并寻求提供洞察力，理论和工具，以在高维度中提供更明智的决策。第一个问题的重点是研究治疗效果异质性。异质治疗效应的量化和表征在评估高维协变量的情况下评估社会计划和医疗治疗的有效性方面起着越来越重要的作用。特别是，该研究将开发有效的程序，以通过协变量平衡来估计异质分位数治疗效果和亚组平均治疗效果。第二个问题的重点是研究数据拆分的有效性和无效性，用于使用高维数据进行推断。当假人/不平衡变量的数量相当大时，该项目将解决“随机分类偏差”的问题。为了克服这一挑战，该项目将开发一个有指导的数据拆分框架，将数据分为更加平衡的半部分。由于随机数据分裂的使用超出了选择后推断，因此开发的框架和从其框架中得出的可能解决方案广泛适用于许多数据驱动的调查。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准来通过评估而被认为是珍贵的支持。