  Counterfactual prediction methods are required when a model will be deployed
in a setting where treatment policies differ from the setting where the model
was developed, or when the prediction question is explicitly counterfactual.
However, estimating and evaluating counterfactual prediction models is
challenging because one does not observe the full set of potential outcomes for
all individuals. Here, we discuss how to tailor a model to a counterfactual
estimand, how to assess the model's performance, and how to perform model and
tuning parameter selection. We also provide identifiability results for
measures of performance for a potentially misspecified counterfactual
prediction model based on training and test data from the same (factual) source
population. Last, we illustrate the methods using simulation and apply them to
the task of developing a statin-na\"{i}ve risk prediction model for
cardiovascular disease.
