An introduction to causal inference for pharmacometricians - James A. Rogers; Hugo Maas; Alejandro Pérez Pitarch (Wiley, 2023)
Abstract As formal causal inference begins to play a greater role in disciplines that intersect with pharmacometrics, such as biostatistics, epidemiology, and artificial intelligence/machine learning, pharmacometricians may increasingly benefit from a basic fluency in foundational causal inference concepts. This tutorial seeks to orient pharmacometricians to three such fundamental concepts: potential outcomes, g‐formula, and directed acyclic graphs (DAGs).