【报告摘要】Directed mixed graphs (DMGs) permit directed and bidirected edges between any two vertices and play an essential role in statistical modeling. This talk discusses the role of such graphs in causal inference and is divided into three parts:

First, I will introduce a matrix algebra for walks on DMGs, motivated by Wright’s path analysis, that allows its user to easily describe and visualize complex graphical concepts. (Based on arXiv:2407.15744.)

Second, I will discuss various interpretations of acyclic DMGs and their relations. Acyclic DMGs are often interpreted as directed acyclic graphs (DAGs) with latent variables. I will introduce a slight modification of this interpretation and argue that it should be used as the default interpretation. (Based on arXiv:2501.03048.)

Third, I will discuss some new approaches to confounder selection and state variable selection in sequential decision problems. Our approach to confounder selection for causal inference does not require the full causal graph. Instead, this approach solicits partial information about the graph in an iterative and interactive fashion. (Based on arXiv:2309.06053, arXiv:2501.00854.)

【报告人简介】Qingyuan was born and raised in Wuhan in central China, a city known for its many lakes and rivers and rich cultural heritage. After high school, he went to the Special Class for the Gifted Young in University of Science and Technology of China and majored in mathematics. He then went to Stanford University for postgraduate studies and obtained his Ph.D. in Statistics in 2016. He spent three years in the Wharton School of University of Pennsylvania as a postdoctoral fellow before joining the Statistical Laboratory in University of Cambridge as a University Lecturer in 2019. He was promoted to Professor of Statistics at University of Cambridge in 2024. Qingyuan’s research interests lie primarily in drawing scientific conclusions about causal relationships using experimental and observational data, a fast-growing area known as “causal inference”. More broadly, he strives to understand how “design”—a principle he views as fundamental yet elusive in statistics—shapes the practice of statistical applications in biomedical and social sciences.