CBMS Conference: Foundations of Causal Graphical Models and Structure Discovery

Texas A&M University, College Station TX

Investigators

Abstract

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.