Explicit causal reasoning is preferred, but not necessary for pragmatic value

Abstract

In researchers Jenkins, Martin, and Peek dis-cuss some of the benefits of applying causal inference frameworks (CIFs) to predict treatment naı¨ve risk in the domain of risk model-ing. We agree that causality-based models using diagrams are a powerful tool and that these models can avoid the pitfalls of model-mediated changes to the outcome process. 1 CIFs have also demon-strated robustness to unobserved confounders. 2 There are many reasons why explicitly considering causality and estimating baseline risk in the absence of treatments are important when deploying and maintaining prognostic models in clinical operations. While these models have many desirable properties, they are not without their challenges, as Sperrin et al note. CIFs demonstrate a firm understanding of the processes one wishes to improve. Getting to the requisite level of insight to build such a diagram is a long and arduous scientific process. This is not to say many processes cannot be dia-gramed using current knowledge. We feel that incorporating causality where it is well understood is useful, but there are circumstances in which CIFs are likely to be incorrect and have the potential to cause er-ror. Furthermore, causal models require data elements that reflect how a process works. Current bulwark data streams (revenue cycle-focused electronic health records) are