Explore the intersection of human knowledge and machine learning in this 57-minute talk by Dana Mackenzie, Simons Institute Journalist-in-Residence. Delve into Bayesian networks, mediation, and confounding factors in data analysis. Examine real-world examples, including the relationship between smoking and cancer, and the challenges of drawing causal conclusions from observational data. Investigate the frontdoor adjustment formula and its applications in causal inference. Discuss the limitations of artificial intelligence, referencing the Uber crash incident, and consider the concept of a causation ladder. Reflect on the importance of understanding causal relationships in an era of increasing reliance on data-driven decision-making.