  Precision medicine involves answering counterfactual questions such as "Would
this patient respond better to treatment A or treatment B?" These types of
questions are causal in nature and require the tools of causal inference to be
answered, e.g., with a structural causal model (SCM). In this work, we develop
an SCM that models the interaction between demographic information, disease
covariates, and magnetic resonance (MR) images of the brain for people with
multiple sclerosis. Inference in the SCM generates counterfactual images that
show what an MR image of the brain would look like if demographic or disease
covariates are changed. These images can be used for modeling disease
progression or used for image processing tasks where controlling for
confounders is necessary.
