  Foundation models are a current focus of attention in both industry and
academia. While they have shown their capabilities in a variety of tasks,
in-depth research is required to determine their robustness to distribution
shift when used as a basis for supervised machine learning. This is especially
important in the context of clinical data, with particular limitations related
to data accessibility, lack of pretraining materials, and limited availability
of high-quality annotations. In this work, we examine the stability of models
based on representations from foundation models under distribution shift. We
focus on confounding by provenance, a form of distribution shift that emerges
in the context of multi-institutional datasets when there are differences in
source-specific language use and class distributions. Using a sampling strategy
that synthetically induces varying degrees of distribution shift, we evaluate
the extent to which representations from foundation models result in
predictions that are inherently robust to confounding by provenance.
Additionally, we examine the effectiveness of a straightforward confounding
adjustment method inspired by Pearl's conception of backdoor adjustment.
Results indicate that while foundation models do show some out-of-the-box
robustness to confounding-by-provenance related distribution shifts, this can
be considerably improved through adjustment. These findings suggest a need for
deliberate adjustment of predictive models using representations from
foundation models in the context of source-specific distributional differences.
