An introduction to causal inference for pharmacometricians
James A. Rogers, Hugo Maas, Alejandro Pérez Pitarch
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).
Topics & Concepts
Causal inferenceInferenceBiostatisticsComputer scienceDirected acyclic graphArtificial intelligenceCognitive scienceCausal modelMachine learningEpistemologyData sciencePsychologyMathematicsEconometricsAlgorithmMedicinePhilosophyStatisticsEpidemiologyInternal medicineStatistical Methods in Clinical TrialsAdvanced Causal Inference TechniquesHealth Systems, Economic Evaluations, Quality of Life