  Instrumental variables (IVs) are widely used for estimating causal effects in
the presence of unmeasured confounding. Under the standard IV model, however,
the average treatment effect (ATE) is only partially identifiable. To address
this, we propose novel assumptions that allow for identification of the ATE.
Our identification assumptions are clearly separated from model assumptions
needed for estimation, so that researchers are not required to commit to a
specific observed data model in establishing identification. We then construct
multiple estimators that are consistent under three different observed data
models, and multiply robust estimators that are consistent in the union of
these observed data models. We pay special attention to the case of binary
outcomes, for which we obtain bounded estimators of the ATE that are guaranteed
to lie between -1 and 1. Our approaches are illustrated with simulations and a
data analysis evaluating the causal effect of education on earnings.
