  Many proposals for the identification of causal effects in the presence of
unmeasured confounding require an instrumental variable or negative control
that satisfies strong, untestable exclusion restrictions. In this paper, we
will instead show how one can identify causal effects for a point exposure by
using a measured confounder as a 'bespoke instrumental variable'. This strategy
requires an external reference population that does not have access to the
exposure, and a stability condition on the confounderoutcome association
between reference and target populations. Building on recent identification
results of Richardson and Tchetgen Tchetgen (2021), we develop the
semiparametric efficiency theory for a general bespoke instrumental variable
model, and obtain a multiply robust locally efficient estimator of the average
treatment effect in the treated. The utility of the estimators is demonstrated
in simulation studies and an analysis of the Life Span Study, concerning atomic
bomb survivors in Japan.
