Using causal modeling to analyze generalization of biomarkers in high-dimensional domains: a case study of adaptive immune repertoires

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 discuss building a diagnostic based on a specific, recently established high-dimensional biomarker – adaptive immune receptor repertoires (AIRRs), and investigate how causal modeling may improve the robustness and generalization of developed diagnostics. We examine how the main biological and experimental factors of the AIRR domain may influence the learned biomarkers, especially in the presence of dataset shifts, and provide simulations of such effects. We conclude that causal modeling could improve AIRR-based diagnostics, but alsothat causal modeling itself might find a powerful testbed with complex, high-dimensional variables in the AIRR field.