  For decades, researchers in fields, such as the natural and social sciences,
have been verifying causal relationships and investigating hypotheses that are
now well-established or understood as truth. These causal mechanisms are
properties of the natural world, and thus are invariant conditions regardless
of the collection domain or environment. We show in this paper how prior
knowledge in the form of a causal graph can be utilized to guide model
selection, i.e., to identify from a set of trained networks the models that are
the most robust and invariant to unseen domains. Our method incorporates prior
knowledge (which can be incomplete) as a Structural Causal Model (SCM) and
calculates a score based on the likelihood of the SCM given the target
predictions of a candidate model and the provided input variables. We show on
both publicly available and synthetic datasets that our method is able to
identify more robust models in terms of generalizability to unseen
out-of-distribution test examples and domains where covariates have shifted.
