时 间：2024年7月29日10:30 - 11:30
地 点：普陀校区理科大楼A1514
报告人：王林勃多伦多大学副教授
主持人：马慧娟华东师范大学副教授
摘 要：
Understanding causal relationships is one of the most important goals of modern science. So far, the causal inference literature has focused almost exclusively on outcomes coming from the Euclidean space R^p. However, it is increasingly common that complex datasets are best summarized as data points in non-linear spaces. In this paper, we present a novel framework of causal effects for outcomes from the Wasserstein space of cumulative distribution functions, which in contrast to the Euclidean space, is non-linear. We develop doubly robust estimators and associated asymptotic theory for these causal effects. As an illustration, we use our framework to quantify the causal effect of marriage on physical activity patterns using wearable device data collected through the National Health and Nutrition Examination Survey.
报告人简介：
王林勃是因果机器学习领域的加拿大研究主席（Canada Research Chair in Causal Machine Learning），同时也是多伦多大学统计科学系和计算机数学科学系的副教授。此外，他在Vector研究所担任教师附属成员（faculty affiliate），并兼任华盛顿大学统计系和多伦多大学计算机科学系的助理教授。他的研究兴趣主要集中在因果推断及其与统计学和机器学习的交互应用上。