CBMS Conference: Foundations of Causal Graphical Models and Structure Discovery

CBMS 会议：因果图模型和结构发现的基础

• 批准号: 2227849
• 负责人: Yang Ni
• 金额: $ 4.03万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227849&HistoricalAwards=false
• 关键词: CBMS; Conference; Foundations; Causal; Graphical

This award supports the Conference Board of the Mathematical Sciences (CBMS) conference “Foundations of Causal Graphical Models and Structure Discovery” hosted by the Department of Statistics at Texas A&M University, May 15-19, 2023. The main lecturer of the conference is Dr. Kun Zhang of the Department of Philosophy, Carnegie Mellon University, an expert in the field of causal discovery and learning. The series of ten lectures and other conference activities are expected to provide investigators and trainees outstanding opportunities to learn and discuss foundational ideas as well as recent advances in causal discovery. The regional emphasis of the conference will strengthen the links and collaborations among research groups and institutions in Texas and will expand research programs in causal discovery.Understanding causality is arguably the ultimate goal in any field of science. Knowledge about causality allows one to predict a system’s behavior under external interventions, a key step towards understanding and engineering that system. While the gold standard for establishing causality remains controlled experimentation, such experimentation is not always possible due to practical or ethical concerns. Therefore, inferring causality from observational data has become an increasingly popular research area attracting researchers from statistics, philosophy, machine learning, artificial intelligence, and data science. The ever-changing field of causal discovery leads to a steep learning curve for students and junior researchers. This conference aims to provide a deep review of causal discovery that will help to orient researchers new to the topic. For more information, please refer to the conference webpage: https://web.stat.tamu.edu/~yni/cbms/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.

该奖项支持数学科学会议委员会（CBMS）会议“因果图形模型和结构发现的基础”，由德克萨斯A M大学统计系主办，2023年5月15日至19日。会议的主讲人是卡内基梅隆大学哲学系的张昆博士，他是因果发现和学习领域的专家。十个系列讲座和其他会议活动预计将提供研究人员和学员学习和讨论的基础思想，以及在因果发现的最新进展的优秀机会。会议的区域重点将加强得克萨斯州研究团体和机构之间的联系和合作，并将扩大因果发现的研究计划。理解因果关系可以说是任何科学领域的最终目标。关于因果关系的知识允许人们预测系统在外部干预下的行为，这是理解和设计该系统的关键一步。虽然建立因果关系的黄金标准仍然是受控实验，但由于实际或道德问题，这种实验并不总是可行的。因此，从观测数据中推断因果关系已经成为一个越来越受欢迎的研究领域，吸引了来自统计学，哲学，机器学习，人工智能和数据科学的研究人员。不断变化的因果发现领域导致学生和初级研究人员的学习曲线陡峭。本次会议旨在提供因果发现的深入审查，这将有助于引导研究人员新的主题。欲了解更多信息，请参阅会议网页：https://web.stat.tamu.edu/~yni/cbms/This奖反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。