  In observational studies, treatments are typically not randomized and
therefore estimated treatment effects may be subject to confounding bias. The
instrumental variable (IV) design plays the role of a quasi-experimental handle
since the IV is associated with the treatment and only affects the outcome
through the treatment. In this paper, we present a novel framework for
identification and inference using an IV for the marginal average treatment
effect amongst the treated (ETT) in the presence of unmeasured confounding. For
inference, we propose three different semiparametric approaches: (i) inverse
probability weighting (IPW), (ii) outcome regression (OR), and (iii) doubly
robust (DR) estimation, which is consistent if either (i) or (ii) is
consistent, but not necessarily both. A closed-form locally semiparametric
efficient estimator is obtained in the simple case of binary IV and outcome and
the efficiency bound is derived for the more general case.
