[gov3009-l] Applied Statistics - 10/21 - Peng Ding

Aaron Kaufman aaronkaufman at fas.harvard.edu
Mon Oct 19 09:35:05 EDT 2015

Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Peng
Ding*, Postdoctoral Fellow in Statistics at Harvard University, and
Assistant Professor of Statistics at Berkeley beginning January 2016.  He
will be presenting work entitled *Sensitivity Analysis Without Assumptions.*
Please find the abstract below and on the website

As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided.  See you all there! To view previous Applied
Statistics presentations, please visit the website

-- Aaron Kaufman

Title: Sensitivity Analysis Without Assumptions

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.
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
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