報告題目：因果圖學習及其在流行病學中的應用
報 告 人：李長城 教授 大連理工大學
報告時間：2023年6月21日 14：00-15：00
報告地點：伟德线上平台第一報告廳
校内聯系人：韓月才 hanyc@jlu.edu.cn
報告摘要：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.
報告人簡介： 李長城，大連理工大學數學科學學院教授。本科就讀于北京大學數學科學學院，獲得統計學學士學位；博士階段師從美國賓夕法尼亞州州立大學統計系李潤澤教授，進行高維統計領域的學習，獲得統計學博士學位。研究興趣主要包括高維統計推斷及高維因果推斷。在高維統計的理論、應用以及計算方面進行了一系列研究，文章發表于一流學術期刊Journal of American Statistical Association、Journal of Econometrics、Annals of Applied Statistics、Statistica Sinica等，入選國家級青年人才計劃。
