Transportability, the ability to maintain performance across populations, is a desirable property of markers of clinical outcomes. However, empirical findings indicate that markers often exhibit varying performances across populations. For prognostic markers that are advertised as predictive risk equations for an outcome of interest, oftentimes a form of updating is required when the equation is transported to populations with different outcome prevalences. Here, we revisit transportability of prognostic markers through the lens of the foundational framework of sufficient component causes (SCC). We argue that transporting a marker "as is" implicitly assumes predictive values are transportable, whereas conventional prevalence adjustment shifts the locus of transportability to accuracy metrics (sensitivity and specificity). Using a minimalist SCC framework that decomposes risk prediction into broad causal constituents, we show that both approaches rely on strong assumptions about the stability of cause distributions. An SCC framework instead invites making transparent assumptions about how different causes vary across populations, leading to different transportation methods. For example, in the absence of any external information other than outcome prevalence, an impartial perspective can assume all causes are responsible for change in prevalence, leading to a new form of marker transportation. Numerical experiments demonstrate that different transportability assumptions lead to varying degrees of information loss, depending on the distribution of causes across populations. An SCC perspective challenges common assumptions and practices for marker transportability, and results in novel transportability methods based on explicit assumptions on how different causes vary across populations.

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