报告题目：Dynamics-based causality inference
报告时间：2022年4月13日, 星期三，上午10:00-12:00
腾讯会议：884-400-069
报告专家：中国科学院上海生物化学与细胞生物学研究所 陈洛南教授
报告摘要：
We present a unified mathematical framework for the so-called dynamical causality (DC), which can detect causal interactions over time, by considering causes and effects from a dynamical perspective. This framework covers Granger causality, transfer entropy, embedding causality and their conditional versions. Based on this framework, we further propose a causality criterion called embedding entropy (EE) to measure the DC between two variables. Moreover, its conditional version, conditional embedding entropy (cEE), is also derived for detecting conditional/direct causality. The significant advantages of EE and cEE are that they can be employed for solving not only nonlinear causal inference but also the non-separability problem, and they can reduce the scale bias in numerical calculation. The performance and robustness of EE and cEE were demonstrated through numerical simulations, and causal inference on various real-world datasets validated their effectiveness.
报告专家简介：
Luonan Chen received BS degree in the Electrical Engineering, from Huazhong University of Science and Technology, and the M.E. and Ph.D. degrees in the electrical engineering, from Tohoku University, Sendai, Japan, in 1988 and 1991, respectively. From 1997, he was an associate professor of the Osaka Sangyo University, Osaka, Japan, and then a full Professor. Since 2010, he has been a professor and executive director at Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences. He was the founding director of Institute of Systems Biology, Shanghai University. He was elected as the founding president of Computational Systems Biology Society of OR China, Chair of Molecular Systems Biology Soceity of CSBMB, and Chair of Technical Committee of Systems Biology at IEEE SMC Society. In recent years, he published over 350 journal papers and two monographs (books) in the area of bioinformatics, nonlinear dynamics and machine learning (H-index >70 ; Citation > 20000).
