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

该研究项目将开发统计方法和工具，用于推断和理解行为和社会科学干预措施的异质性影响的影响。社会与行为科学实验研究面临着危机和机遇。基于初步研究似乎有希望的行为干预措施未能复制或在大规模实施时令人失望。忽略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CAREER项目将开发贝叶斯方法，用于估计大型复杂数据集中的异质性治疗效果。将开发的方法将应用于现实世界的行为干预。研究人员将与得克萨斯州行为科学与政策研究所和得克萨斯大学的OnRamps计划合作，开发和评估旨在促进高中数学教师和大学教师的成长心态实践和信念的干预措施。此外，还将指导学生，并将开发和免费提供案例研究和软件。 该奖项由MMS计划和教育和人力资源局的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。估计干预措施的异质性效应是一个具有挑战性的统计问题，特别是当现有的科学知识（甚至理论）的影响如何因个人和背景而异是缺乏。贝叶斯树模型结合了联合收割机贝叶斯统计和机器学习的预测方法，已被证明是在大规模实证评估中推断异质效应的最有效方法。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。从这些复杂的模型中提取可操作的见解也很困难，这对于指导未来干预措施或研究的设计以及了解大规模部署干预措施的影响是必要的。该项目将开发树木合奏，以鼓励（部分）平滑估计的治疗效果，着眼于计算成本。该项目还将使贝叶斯树模型适应更复杂的数据的异质性影响。将开发的方法将用于改进行为研究的设计和分析。该项目的成果将促进统计学、机器学习、数据科学、教育和行为科学领域的知识发展。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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 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.