  One of the fundamental challenges in drawing causal inferences from
observational studies is that the assumption of no unmeasured confounding is
not testable from observed data. Therefore, assessing sensitivity to this
assumption's violation is important to obtain valid causal conclusions in
observational studies. Although several sensitivity analysis frameworks are
available in the casual inference literature, very few of them are applicable
to observational studies with multivalued treatments. To address this issue, we
propose a sensitivity analysis framework for performing sensitivity analysis in
multivalued treatment settings. Within this framework, a general class of
additive causal estimands has been proposed. We demonstrate that the estimation
of the causal estimands under the proposed sensitivity model can be performed
very efficiently. Simulation results show that the proposed framework performs
well in terms of bias of the point estimates and coverage of the confidence
intervals when there is sufficient overlap in the covariate distributions. We
illustrate the application of our proposed method by conducting an
observational study that estimates the causal effect of fish consumption on
blood mercury levels.
