  In this paper, we consider the extent of the biases that may arise when an
unmeasured confounder is omitted from a structural equation model (SEM) and we
propose sensitivity analysis techniques to correct for such biases. We give an
analysis of which effects in an SEM are, and are not, biased by an unmeasured
confounder. It is shown that a single unmeasured confounder will bias not just
one, but numerous, effects in an SEM. We present sensitivity analysis
techniques to correct for biases in total, direct, and indirect effects when
using SEM analyses, and illustrate these techniques with a study of aging and
cognitive function.
