The “Causal AI Modeling” course introduces students to the critical principles and techniques of identifying and modeling cause-and-effect relationships using AI. Unlike traditional correlation-based approaches, this course emphasizes causal inference, enabling more robust and interpretable decision-making in diverse applications such as healthcare, economics, and social sciences. Students will learn how to construct, validate, and apply causal models using real-world data while exploring cutting-edge algorithms and tools that drive AI systems capable of reasoning about interventions and outcomes. Hands-on projects will deepen their understanding of how to build more transparent and actionable AI solutions.