  Background and Aims: The methods with which prediction models are usually
developed mean that neither the parameters nor the predictions should be
interpreted causally. However, when prediction models are used to support
decision making, there is often a need for predicting outcomes under
hypothetical interventions. We aimed to identify published methods for
developing and validating prediction models that enable risk estimation of
outcomes under hypothetical interventions, utilizing causal inference: their
main methodological approaches, underlying assumptions, targeted estimands, and
potential pitfalls and challenges with using the method, and unresolved
methodological challenges.
  Methods: We systematically reviewed literature published by December 2019,
considering papers in the health domain that used causal considerations to
enable prediction models to be used for predictions under hypothetical
interventions.
  Results: We identified 4919 papers through database searches and a further
115 papers through manual searches, of which 13 were selected for inclusion,
from both the statistical and the machine learning literature. Most of the
identified methods for causal inference from observational data were based on
marginal structural models and g-estimation.
  Conclusions: There exist two broad methodological approaches for allowing
prediction under hypothetical intervention into clinical prediction models: 1)
enriching prediction models derived from observational studies with estimated
causal effects from clinical trials and meta-analyses; and 2) estimating
prediction models and causal effects directly from observational data. These
methods require extending to dynamic treatment regimes, and consideration of
multiple interventions to operationalise a clinical decision support system.
Techniques for validating 'causal prediction models' are still in their
infancy.
