  Sensitivity to unmeasured confounding is not typically a primary
consideration in designing treated-control comparisons in observational
studies. We introduce a framework allowing researchers to optimize robustness
to omitted variable bias at the design stage using a measure called design
sensitivity. Design sensitivity, which describes the asymptotic power of a
sensitivity analysis, allows transparent assessment of the impact of different
estimation strategies on sensitivity. We apply this general framework to two
commonly-used sensitivity models, the marginal sensitivity model and the
variance-based sensitivity model. By comparing design sensitivities, we
interrogate how key features of weighted designs, including choices about
trimming of weights and model augmentation, impact robustness to unmeasured
confounding, and how these impacts may differ for the two different sensitivity
models. We illustrate the proposed framework on a study examining drivers of
support for the 2016 Colombian peace agreement.
