报告人：谢峰
报告地点：数学与统计学院415室
报告时间：2024年08月27日星期二10:00-11:00
报告摘要：
Discovering causal relationships from observational data, particularly in the presence of latent variables, poses a challenging problem. While numerous effective methods have been proposed, it is often unnecessary and wasteful to find the global structures when our interest lies solely in the local structure of one target variable. Current local structure learning methods largely assume causal sufficiency, meaning that all the common causes of the measured variables are observed. This talk first show how to locally identify potential parent and child nodes of a target node within models that include latent variables. Specifically, we provide a principled method for determining whether a variable is a cause or non-cause of a target, based solely on the local structure rather than the entire graph. Next, we demonstrate how to locally estimate the causal effects of parent nodes on the target node. Theoretically, we demonstrate the correctness of our approach under the assumptions of faithfulness, and the accurate checking of independencies.
主讲人简介：
谢峰，北京工商大学 数学与统计学院副教授、应用统计系系主任，硕士生导师。研究领域涵盖因果推断与人工智能理论，特别是因果发现机制、隐变量因果表达学习及其在社会学、经济学和生物学中的应用。研究成果在ICML、NeurIPS、ICLR、AAAI、IJCAI和JMLR、TNNLS、Neurocomputing等国内外顶级学术会议和权威期刊发表30多篇论文。
