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sidenav header background[4月1日] 计量金融与大数据分析workshop
发布日期：2022-03-28 02:54 来源：
A Focusing Framework for Testing Bi-Directional Causal Effects with GWAS Summary Data（孟德尔随机化实验中双向因果关系的假设检验）
主讲人：李赛（中国人民大学统计与大数据研究院）
主持人：（国发院）沈艳、黄卓、孙振庭、张俊妮
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆
时间：2022年4月1日（周五）10:00 AM -- 11:30 AM
地点：承泽园245教室
报告摘要：
Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables (IVs) to infer the causal effect of a modifiable exposure on an outcome. Although recent years have seen many extensions of basic MR methods to be robust to certain violations of assumptions, few methods were proposed to infer bi-directional causal relationships, especially for phenotypes with limited biological understandings. The presence of horizontal pleiotropy adds another layer of complexity. We show that assumptions for common MR methods are often impossible or too stringent in the existence of bi-directional relationships. We then propose a new focusing framework for testing bi-directional causal effects between two traits with possibly pleiotropic genetic variants. We provide theoretical guarantees on the Type I error and power of the proposed methods. We demonstrate the robustness of the proposed methods using several simulated and real datasets. This is joint work with Ting Ye.
主讲人简介：
李赛，中国人民大学统计与大数据研究院助理教授，博士生导师。2018年于罗格斯新泽西州立大学获得统计博士学位，毕业后于宾夕法尼亚大学生物统计系和统计系进行博士后研究，目前的研究方向包括高维数据分析、迁移学习、因果推断的统计方法及理论和在遗传学、流行病学和机器学习中的应用。
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教学项目展开 / 收起
sidenav background教学项目
sidenav header background[4月1日] 计量金融与大数据分析workshop
发布日期：2022-03-28 02:54 来源：
A Focusing Framework for Testing Bi-Directional Causal Effects with GWAS Summary Data（孟德尔随机化实验中双向因果关系的假设检验）
主讲人：李赛（中国人民大学统计与大数据研究院）
主持人：（国发院）沈艳、黄卓、孙振庭、张俊妮
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆
时间：2022年4月1日（周五）10:00 AM -- 11:30 AM
地点：承泽园245教室
报告摘要：
Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables (IVs) to infer the causal effect of a modifiable exposure on an outcome. Although recent years have seen many extensions of basic MR methods to be robust to certain violations of assumptions, few methods were proposed to infer bi-directional causal relationships, especially for phenotypes with limited biological understandings. The presence of horizontal pleiotropy adds another layer of complexity. We show that assumptions for common MR methods are often impossible or too stringent in the existence of bi-directional relationships. We then propose a new focusing framework for testing bi-directional causal effects between two traits with possibly pleiotropic genetic variants. We provide theoretical guarantees on the Type I error and power of the proposed methods. We demonstrate the robustness of the proposed methods using several simulated and real datasets. This is joint work with Ting Ye.
主讲人简介：
李赛，中国人民大学统计与大数据研究院助理教授，博士生导师。2018年于罗格斯新泽西州立大学获得统计博士学位，毕业后于宾夕法尼亚大学生物统计系和统计系进行博士后研究，目前的研究方向包括高维数据分析、迁移学习、因果推断的统计方法及理论和在遗传学、流行病学和机器学习中的应用。
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