  Over the last decades, many prognostic models based on artificial
intelligence techniques have been used to provide detailed predictions in
healthcare. Unfortunately, the real-world observational data used to train and
validate these models are almost always affected by biases that can strongly
impact the outcomes validity: two examples are values missing not-at-random and
selection bias. Addressing them is a key element in achieving transportability
and in studying the causal relationships that are critical in clinical decision
making, going beyond simpler statistical approaches based on probabilistic
association.
  In this context, we propose a novel approach that combines selection
diagrams, missingness graphs, causal discovery and prior knowledge into a
single graphical model to estimate the cardiovascular risk of adolescent and
young females who survived breast cancer. We learn this model from data
comprising two different cohorts of patients. The resulting causal network
model is validated by expert clinicians in terms of risk assessment, accuracy
and explainability, and provides a prognostic model that outperforms competing
machine learning methods.
