主 题：Sensitivity Analysis Without Assumptions
主讲人：Dr. Peng Ding
主持人：林华珍教授
时 间：2015年12月10日星期四下午4：00-5：00
地 点：通博楼B212学术会议室
主办单位：统计研究中心 统计学院 科研处
主讲人简介：
Dr.Peng Ding 2015年毕业于Harvard University，即将任教于University of California, Berkeley, 主要研究方向为因果推断。已发表或接收待发表文章共计19篇，其中在统计学顶级期刊JASA,JRSS(B), Biometrika等共计7篇。荣获ASA Student Paper Competition Award 以及Institute of Mathematical Statistics Travel Award等多个奖项。
内容提要：
Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on the causal conclusions. However, previous sensitivity analysis approaches often make strong and untestable assumptions such as having a confounder that is binary, or having no interaction between the effects of the exposure and the confounder on the outcome, or having only one confounder. Without imposing any assumptions on the confounder or confounders, we derive a bounding factor and a sharp inequality such that the sensitivity analysis parameters must satisfy the inequality if an unmeasured confounder is to explain away the observed effect estimate or reduce it to a particular level. Our approach is easy to implement and involves only two sensitivity parameters. Surprisingly, our bounding factor, which makes no simplifying assumptions, is no more conservative than a number of previous sensitivity analysis techniques that do make assumptions. Our new bounding factor implies not only the traditional Cornfield conditions that both the relative risk of the exposure on the confounder and that of the confounder on the outcome must satisfy, but also a high threshold that the maximum of these relative risks must satisfy. Furthermore, this new bounding factor can be viewed as a measure of the strength of confounding between the exposure and the outcome induced by a confounder.
