The article provides a practical guide to causal structure learning using Bayesian methods in Python. It covers essential concepts, techniques, and implementations that enable readers to effectively analyze causal relationships in their data. This resource is tailored for data professionals looking to deepen their understanding of causal inference.

A novel diffusion-based method called DIME is introduced for learning the joint distribution of multiple interdependent medical treatment outcomes. DIME addresses the limitations of existing machine learning approaches by capturing dependence structures and handling mixed outcome types, thereby enabling more reliable decision-making with uncertainty quantification. Experimental results demonstrate its effectiveness in learning the multi-outcome distribution of medical treatments.