FIGURE 1.

Basic causal model of proposed relationships between measured variables in the CCBP cohort study. Arrows between variables (in circles) indicate the dominant direction of causal influence between them. In this study, machine learning is used to model predictive relationships, but these should also be causal, not merely associational, relationships. For example, in predicting phenotype (effect) from omics data (causes), confounders such as subject age influence both cause and effect variables, which makes it critical to take these into consideration when using predictive machine learning algorithms.