From Causal Loop Diagrams to System Dynamics Models in a Data-Rich Ecosystem


The lack of global data flow in healthcare systems negatively impacts decision-making both locally and globally. This Chapter aims to introduce global health specialists to causal loop diagrams (CLDs) and system dynamics models to help them better frame, examine, and understand complex issues characteristic to data-rich ecosystems. As machine and statistical learning tools become popular among data scientists and researchers, they can help us understand how various data sources and variables interact with each other mechanistically. These complementary approaches go a step beyond machine and statistical learning tools to represent causality between variables affecting data-driven ecosystems and decision-making.

MIDAS Network Members