报告题目：因果图学习及其在流行病学中的应用
报 告 人：李长城 教授 大连理工大学
报告时间：2023年6月21日 14：00-15：00
报告地点：英国365网站第一报告厅
校内联系人：韩月才 hanyc@jlu.edu.cn
报告摘要：The Population-based HIV Impact Assessment (PHIA) is an ongoing project that conducts nationally representative HIV-focused surveys for measuring national and regional progress toward UNAIDS’90-90-90 targets, the primary strategy to end the HIV epidemic. We believe the PHIA survey offers a unique opportunity to better understand the key factors that drive the HIV epidemics in the most affected countries in sub-Saharan Africa. In this article, we propose a novel causal structural learning algorithm to discover important covariates and potential causal pathways for 90-90-90 targets.
Existing constrained-based causal structural learning algorithms are quite aggressive in edge removal. The proposed algorithm preserves more information about important features and potential causal pathways. It is applied to the Malawi PHIA (MPHIA) data set and leads to interesting results. We further compare and validate the proposed algorithm using BIC and using Monte Carlo simulations, and show that the proposed algorithm achieves improvement in true positive rates in important feature discovery over existing algorithms.
报告人简介： 李长城，大连理工大学数学科学学院教授。本科就读于北京大学数学科学学院，获得统计学学士学位；博士阶段师从美国宾夕法尼亚州州立大学统计系李润泽教授，进行高维统计领域的学习，获得统计学博士学位。研究兴趣主要包括高维统计推断及高维因果推断。在高维统计的理论、应用以及计算方面进行了一系列研究，文章发表于一流学术期刊Journal of American Statistical Association、Journal of Econometrics、Annals of Applied Statistics、Statistica Sinica等，入选国家级青年人才计划。
