Explore advanced concepts in Bayesian Networks for causal reasoning in this comprehensive lecture. Delve into the application of Bayesian methods in healthcare and disease modeling as part of the "Machine Learning for Health and Disease" program. Learn how to leverage these powerful tools to analyze clinical data, predict patient outcomes, and infer patterns in large-scale heterogeneous datasets. Gain insights from expert Tavpritesh Sethi on bridging the gap between mathematical modeling and real-world clinical problems. Suitable for PhD students in STEM fields, medical students, postdoctoral fellows, faculty, and professionals in science, engineering, and medicine seeking to enhance their understanding of machine learning techniques in biomedicine and public health.