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CAREER: Bayesian Tree Models for Next-Generation Studies in the Behavioral and Social Sciences

职业：行为和社会科学下一代研究的贝叶斯树模型

基本信息

批准号：
2046896
负责人：
Jared Murray
金额：
$ 45万
依托单位：
University of Texas at Austin
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：
2021-07-01 至 2026-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046896&HistoricalAwards=false
关键词：
CAREER Bayesian Tree Models Next

项目摘要

This research project will develop statistical methods and tools for inferring and understanding the implications of heterogenous effects of interventions in the behavioral and social sciences. Experimental research in the social and behavioral science is facing a crisis and an opportunity. Behavioral interventions that seemed promising based on initial studies have failed to replicate or have disappointed when implemented at scale. Ignoring heterogeneous effects across individuals and by contexts is a likely contributor of these results. This CAREER project will develop Bayesian methods for estimating heterogeneous treatment effects in large, complicated datasets. The methods to be developed will be applied to real-world behavioral interventions. The investigator will collaborate with the Texas Behavioral Science and Policy Institute and the University of Texas's OnRamps program to develop and evaluate interventions designed to promote growth mindset practices and beliefs among high school math teachers and college instructors. In addition, students will be mentored, and case studies and software will be developed and made freely available. This award is supported by the MMS program and the Education and Human Resources directorate's ECR program.This research project will develop Bayesian tree priors, models, and computational methods for complex study designs. Estimating heterogeneous effects of interventions is a challenging statistical problem, particularly when existing scientific knowledge (or even theories) about how effects vary by individuals and by context is lacking. Bayesian tree models, which combine Bayesian statistics and predictive methods from machine learning, haven proven to be some of the most effective methods for inferring heterogenous effects in large-scale empirical evaluations. However, current methods are limited to simple data structures, study designs, and outcomes, limiting their real-world applicability. It also can also be difficult to extract actionable insights from these sophisticated models, which is necessary for guiding the design of future interventions or studies and for understanding the implications of deploying an intervention at scale. This project will develop tree ensembles to encourage (partial) smoothing in estimated treatment effects with an eye to computational costs. The project also will adapt Bayesian tree models for heterogeneous effects to more complex data. The methods to be developed will be used to improve both the design and analysis of behavioral studies. The results of this project will advance knowledge in the fields of statistics, machine learning, data science, education, and behavioral science.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.

这一研究项目将开发统计方法和工具，以推断和理解行为和社会科学干预措施的异质性影响的含义。社会和行为科学的实验研究面临着危机和机遇。基于最初研究的行为干预似乎很有希望，但在大规模实施时，要么未能复制，要么令人失望。忽略不同个体和背景的异质性影响可能是这些结果的一个贡献者。这个职业项目将开发贝叶斯方法，在大型、复杂的数据集中估计不同种类的治疗效果。将要开发的方法将应用于现实世界的行为干预。这位研究人员将与德克萨斯行为科学与政策研究所和德克萨斯大学的OnRamps项目合作，开发和评估旨在促进高中数学教师和大学教师成长的心态、做法和信念的干预措施。此外，还将指导学生，开发案例研究和软件，并免费提供。该奖项由MMS计划和教育与人力资源管理局的ECR计划支持。该研究项目将为复杂的研究设计开发贝叶斯树先验、模型和计算方法。估计干预措施的不同影响是一个具有挑战性的统计学问题，特别是在缺乏关于影响如何因个人和环境而异的现有科学知识(甚至理论)的情况下。贝叶斯树模型结合了贝叶斯统计和机器学习中的预测方法，已被证明是在大规模经验评估中推断异质效应的最有效方法之一。然而，目前的方法仅限于简单的数据结构、研究设计和结果，限制了它们在现实世界中的适用性。也很难从这些复杂的模型中提取可操作的见解，这对于指导未来干预或研究的设计以及理解大规模部署干预的影响是必要的。该项目将开发树集合，以鼓励在考虑到计算成本的情况下(部分)平滑估计的治疗效果。该项目还将使贝叶斯树模型适用于更复杂的数据，以适应不同的效果。将要开发的方法将用于改进行为研究的设计和分析。该项目的结果将促进统计学、机器学习、数据科学、教育和行为科学领域的知识。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。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Do forecasts of bankruptcy cause bankruptcy? A machine learning sensitivity analysis

DOI：
10.1214/22-aoas1648
发表时间：
2021-06
期刊：
The Annals of Applied Statistics
影响因子：
0
作者：
Demetrios V. Papakostas;P. Hahn;Jared S. Murray;Frank S. Zhou;Joseph J. Gerakos
通讯作者：
Demetrios V. Papakostas;P. Hahn;Jared S. Murray;Frank S. Zhou;Joseph J. Gerakos

Adaptive Conditional Distribution Estimation with Bayesian Decision Tree Ensembles