数据科学的数学基础，这个在线研讨会不可错过 Online Seminar on Mathematical Foundations of Data Science (Math for DS) [1] 是在线的、每周举办的系列研讨会。研讨会旨在讨论数据科学、机器学习、统计以及优化背后的数学原理，邀请了北美诸多知名学者进行主题演讲。『运筹 OR 帷幄』、『机器之心』作为合作媒体，将在 B 站 / 知乎为大家带来直播并在 B 站发布往期的回放视频。 Online Seminar on Mathematical Foundations of Data Science（Math4DS）是在线的、每周举办的系列研讨会，其内容涵盖数据科学、机器学习、统计以及优化背后的数学基础。 在线研讨会将在 Zoom 上进行，链接：https://psu.zoom.us/s/95512102924 有关研讨会的公告可通过点击阅读原文，链接到国内镜像网址获得。 研讨会邀请到诸多知名学者进行主题演讲，目前受邀参加的有： Math for DS 第十七期线上直播预告 主题：On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning 嘉宾：James Robins 时间：北京时间 9 月 2 日凌晨三点 地点：Zoom 主题介绍 For many causal effect parameters of interest, doubly robust machine learning (DRML) estimators are the state-of-the-art, incorporating the good prediction performance of machine learning; the decreased bias of doubly robust estimators; and the analytic tractability and bias reduction of sample splitting with cross fitting. Nonetheless, even in the absence of confounding by unmeasured factors, the nominal Wald confidence interval may still undercover even in large samples, because the bias may be of the same or even larger order than its standard error. In this paper, we introduce essentially assumption-free tests that (i) can falsify the null hypothesis that the bias is of smaller order than its standard error, (ii) can provide a upper confidence bound on the true coverage of the Wald interval, and (iii) are valid under the null under no smoothness/sparsity assumptions on the nuisance parameters. The tests, which we refer to as Assumption Free Empirical Coverage Tests (AFECTs), are based on a U-statistic that estimates part of the bias. Our claims need to be tempered in several important ways. First no test, including ours, of the null hypothesis that the ratio of the bias to its standard error is smaller than some threshold can be consistent [with-out additional assumptions (e.g. smoothness or sparsity) that may be incorrect]. Second the above claims only apply to certain parameters in a particular class. For most of the others, our results are unavoidably less sharp. 嘉宾介绍 James M. Robins，流行病学家和生物统计学家，因提出从复杂的观察性研究和随机试验中得出因果推断的先进方法，尤其是治疗方法随时间变化的因果推断而闻名。他因其在统计和流行病学领域的终生成就而荣获 2013 年内森 · 曼特奖。 他于 1976 年毕业于圣路易斯华盛顿大学的医学专业。目前，他是哈佛大学公共卫生学院 Mitchell L. 和 Robin LaFoley Dong 流行病学教授。他在学术期刊上发表了 100 多篇论文，并且是 ISI 受到高度引用的研究员。 如何观看 B 站录播？ 受北美教授的时间限制，Math4DS 每期研讨会时间大多设置在美东时间周二的下午三点，即北京时间周三的凌晨三点。这对于国内的观众非常不友好，但是『运筹 OR 帷幄』也在 B 站提供了每期的录播，错过直播的小伙伴和想要回顾的小伙伴可以在前往 B 站观看，小编也会在第一时间上传最新的研讨会视频。 B 站官方号：https://space.bilibili.com/403058474 研讨会主办方简介 组织者： Ethan X. Fang, Niao He, Junwei Lu, Zhaoran Wang, Zhuoran Yang, Tuo Zhao 赞助方： 参考文献[1]https://sites.google.com/view/seminarmathdatascience/home阅读原文