  We introduce several methods for assessing sensitivity to unmeasured
confounding in marginal structural models; importantly we allow treatments to
be discrete or continuous, static or time-varying. We consider three
sensitivity models: a propensity-based model, an outcome-based model, and a
subset confounding model, in which only a fraction of the population is subject
to unmeasured confounding. In each case we develop efficient estimators and
confidence intervals for bounds on the causal parameters.
