RTG: Advancing Machine Learning - Causality and Interpretability

加州大学伯克利分校统计系的教师开发了一个研究和教育的综合计划，以支持本科生的研究经历、研究生研究实习生和博士后奖学金。培训活动的共同研究主题是如何利用统计机器学习的预测能力来解决因果关系和可解释性问题。该项目旨在为下一代统计学家和数据科学家做好准备，以解决因分析海量数据而出现的新的重要问题。从直觉上看，似乎可以从更大的数据集中得出更可靠、更准确的推断。然而，决定和干预必须是可解释的，并以不确定性的统计衡量为理由，这在这种情况下是具有挑战性的。该项目将以一种整合的方式在教育项目的所有层面注入想法、能量和资源，从本科专业到博士后经验，招收学生，并为他们参与这个令人兴奋的新领域的非凡机会做好准备。该项目的研究将追求理论，以弥合因果推理和机器学习研究之间的差距，包括高维推理、多重测试、带有干扰的因果推理、因果关系和基因表达。该项目处于统计和数据科学的前沿，弥合了机器学习和因果推理之间的鸿沟，其潜在影响远远超出了统计学学科。计划通过研究生导师计划重新设计和扩大本科生参与研究的机会；设计研究生和本科生层面的新课程，包括一门建立在数据科学、社会科学和伦理学之间联系的入门课程；以及通过研究研讨会加强研究生研究培训。该计划将包括研究生职业发展培训系列，涉及技术、演讲和写作技能以及建立包容性科学社区等主题。该项目还将为研究生和博士后助理提供重要的教学培训。通过多种渠道的结合，培训方面的创新将推广到其他机构和学科，例如，在以因果推理为中心的政策和教育环境中展示机器学习的力量。该计划还包括开发教育材料，并计划在更广泛的社区广泛传播这些材料。该项目将强调旨在增加国内学生多样性的招生和留住努力。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。

Faculty in the Statistics Department at the University of California, Berkeley develop an integrated program of research and education to support undergraduate research experiences, graduate research traineeships, and postdoctoral fellowships. The common research theme of the training activities is how to leverage the predictive power of statistical machine learning to address questions of causality and interpretability. The project aims to prepare the next generation of statisticians and data scientists to tackle new, important problems that arise from the analysis of massive data. Intuitively it seems that more reliable and precise inferences can be drawn from larger data sets. However, decisions and interventions must be interpretable and justified by statistical measures of uncertainty, which are challenging in this setting. This program will infuse ideas, energy, and resources in an integrated way at all levels of the educational program, from the undergraduate major to the postdoctoral experience, recruiting students and preparing them to participate in the extraordinary range of opportunities in this exciting new field.The research in this project will pursue theory to bridge the gap between causal inference and machine learning research, including high-dimensional inference, multiple testing, causal inference with interference, and causality and gene expression. The project is at the frontiers of statistics and data science, bridging the divide between machine learning and causal inference with potential impact far beyond the discipline of statistics. Plans are to redesign and expand the engagement of undergraduates in research through a graduate student mentorship program; to design new courses at the graduate and undergraduate levels, including an introductory course that builds on connections between data science, social sciences, and ethics; and to enhance graduate research training via a research symposium. The program will include a graduate professional development training series that addresses topics in technology, presentation and writing skills, and building an inclusive science community. The project will also provide significant training in teaching for graduate students and postdoctoral associates. Through a combination of channels, the innovations in training will spread to other institutions and disciplines, e.g., demonstrating the power of machine learning in policy and education settings where causal inference is central. The program also includes the development of educational materials with plans to disseminate them widely throughout the broader community. The project will emphasize recruitment and retention efforts targeted to increase the diversity of domestic students.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.