  Background: In epidemiology, causal inference and prediction modeling
methodologies have been historically distinct. Directed Acyclic Graphs (DAGs)
are used to model a priori causal assumptions and inform variable selection
strategies for causal questions. Although tools originally designed for
prediction are finding applications in causal inference, the counterpart has
remained largely unexplored. The aim of this theoretical and simulation-based
study is to assess the potential benefit of using DAGs in clinical risk
prediction modeling.
  Methods and Findings: We explore how incorporating knowledge about the
underlying causal structure can provide insights about the transportability of
diagnostic clinical risk prediction models to different settings. A
single-predictor model in the causal direction is likely to have better
transportability than one in the anticausal direction. We further probe whether
causal knowledge can be used to improve predictor selection. We empirically
show that the Markov Blanket, the set of variables including the parents,
children, and parents of the children of the outcome node in a DAG, is the
optimal set of predictors for that outcome.
  Conclusions: Our findings challenge the generally accepted notion that a
change in the distribution of the predictors does not affect diagnostic
clinical risk prediction model calibration if the predictors are properly
included in the model. Furthermore, using DAGs to identify Markov Blanket
variables may be a useful, efficient strategy to select predictors in clinical
risk prediction models if strong knowledge of the underlying causal structure
exists or can be learned.
