CBMS Conference: Foundations of Causal Graphical Models and Structure Discovery

该奖项支持于2023年5月15日至19日在德克萨斯农工大学统计系主办的数学科学(CBMS)会议的会议《因果图形模型和结构发现的基础》。大会主讲嘉宾是卡内基梅隆大学哲学系的因果发现与学习领域专家张坤博士。十场系列讲座和其他会议活动预计将为研究人员和受训人员提供学习和讨论基本思想以及因果发现方面的最新进展的绝佳机会。会议的地区性重点将加强德克萨斯州研究小组和机构之间的联系和合作，并将扩大因果发现方面的研究计划。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使人们能够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。虽然确定因果关系的黄金标准仍然是受控实验，但出于实际或伦理方面的考虑，这样的实验并不总是可能的。因此，从观测数据推断因果关系已经成为一个日益热门的研究领域，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。本次会议旨在提供对因果发现的深入回顾，这将有助于引导新的研究人员了解这一主题。更多信息，请参考会议网页：https://web.stat.tamu.edu/~yni/cbms/This奖项反映了美国国家科学基金会的法定使命，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。

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.