source：Statistics School of SWUFE Release date：2015-10-08 Views：414
Theme：Sensitivity Analysis Without Assumptions
Speaker：Dr. Peng Ding
Hosted by：Prof. Huazhen Lin
Time：PM16:00-17：00, Thursday, December 10, 2015
Place： Academic Meeting Room ( B212 in Tongbo Building )
Dr.Peng Dinggraduated from Harvard University in 2015and willteach for University of California, Berkeley,whose main research direction is the causal inference. His 19 papers have been published or received, 7 of which appear in statistics top journals such as JASA,JRSS(B), Biometrika. He has won several awards including ASA Student Paper Competition Award and Institute of Mathematical Statistics Travel Award.
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.