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发布时间:2023-06-24文章来源: 浏览次数:

报告题目: Causal Inference on Distribution Functions


报告摘要: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 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.

报告时间:7 月 7 日(周五)下午 16:50—17:40

报告地点:统计与数据科学学院 213 会议室

报告人简介:孔德含,多伦多大学统计学教授,研究方向包括脑图像,函数型数据分析,因果推断,高维数据分析以及机器学习。研究成果发表在统计学国际顶级期刊 JRSSB,JASA, Biometrika 等,现任统计学顶级期刊 JASA 副主编

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