  Instrumental variable (IV) methods are becoming increasingly popular as they
seem to offer the only viable way to overcome the problem of unobserved
confounding in observational studies. However, some attention has to be paid to
the details, as not all such methods target the same causal parameters and some
rely on more restrictive parametric assumptions than others. We therefore
discuss and contrast the most common IV approaches with relevance to typical
applications in observational epidemiology. Further, we illustrate and compare
the asymptotic bias of these IV estimators when underlying assumptions are
violated in a numerical study. One of our conclusions is that all IV methods
encounter problems in the presence of effect modification by unobserved
confounders. Since this can never be ruled out for sure, we recommend that
practical applications of IV estimators be accompanied routinely by a
sensitivity analysis.
