  Machine learning is increasingly used to discover diagnostic and prognostic
biomarkers from high-dimensional molecular data. However, a variety of factors
related to experimental design may affect the ability to learn generalizable
and clinically applicable diagnostics. Here, we argue that a causal perspective
improves the identification of these challenges and formalizes their relation
to the robustness and generalization of machine learning-based diagnostics. To
make for a concrete discussion, we focus on a specific, recently established
high-dimensional biomarker - adaptive immune receptor repertoires (AIRRs).
Through simulations, we illustrate how major biological and experimental
factors of the AIRR domain may influence the learned biomarkers. In conclusion,
we argue that causal modeling improves machine learning-based biomarker
robustness by identifying stable relations between variables and by guiding the
adjustment of the relations and variables that vary between populations.
