A Causal Modeling Framework for Generating Clinical Practice Guidelines from Data

The practice of medicine is becoming increasingly evidencebased and clinical practice guidelines (CPGs) are necessary for advancing evidence-based medicine (EBM). We hypothesize that machine learning methods can play an important role in learning CPGs automatically from data . Automatically induced CPGs can then be used for further manual refinement and deployment, for automated guideline compliance checking, for better understanding of disease processes, and for improved physician education. We discuss why learning CPGs is a special form of computational causal discovery and why simply predictive (i.e., noncausal) methods may not be appropriate for this task.