Foundations of Causal AI

This unit introduces the principles, history, and business rationale for causal artificial intelligence and contrasts it with traditional analytics. Students will explore core concepts, the role of hybrid teams, data and visualization needs, and practical frameworks for initiating causal AI projects.

Learning Objectives

• Analyze the differences between causal AI and traditional analytics by comparing methodologies, expected outputs, and implications for decision-making using provided case examples
• Evaluate the business value, promises, and ethical considerations of adopting causal AI, including the role of augmented intelligence in human–AI decision workflows
• Demonstrate knowledge of core causal AI concepts and origins by accurately defining key terms (e.g., causation vs. correlation, causal models, interventions) and summarizing the historical evolution of the field
• Apply foundational frameworks to design a preliminary causal AI initiative by specifying scope of services, required data and visualizations, and hybrid team roles and responsibilities