Section outline

  • Lesson Goal: Explain the difference between correlation and causation, why traditional ML struggles with causal relationships, and introduce tools of causal inference (experiments and causal models) that are increasingly being integrated with machine learning to create AI that truly understands cause and effect.

    • ·         Micro-topic 22.1: Correlation vs. Causation – Why the Difference Matters (Goal: Ensure students grasp that “correlation does not imply causation,” using relatable examples to show why ML models that only learn correlations can be fooled)

    • L22 MT1 FREE Correlation vs Causation SCORM12 SCORM package
      • Micro-topic 22.2: The Basics of Causal Inference (Goal: Introduce what causal inference means – determining cause-effect relationships – and basic concepts like randomized experiments and causal graphs in simple terms)
    • L22 MT2 FREE Basics Causal Inference SCORM12 SCORM package
      • Micro-topic 22.3: Integrating Causal Thinking into Machine Learning (Goal: Show how causal inference ideas are being used to address ML’s limitations – e.g. improving generalization, fairness, and interpretability by focusing on cause-effect rather than pure correlation)
    • L22 MT3 FREE Causal Thinking in ML SCORM12 SCORM package
      • Micro-topic 22.4: Causal Inference in Action – Examples (Goal: Provide tangible examples where causal inference improved ML outcomes or insights, reinforcing the lesson with stories or case studies)
    • L22 MT4 FREE Causal Inference Examples SCORM12 SCORM package
      • Micro-topic 22.5: Looking Ahead – Causal AI’s Role in Your Future (Goal: Conclude the lesson by connecting causal inference to the “win against AI” theme, inspiring students to appreciate that mastering causality gives them a leg up in developing and working with future AI)
    • L22 MT5 FREE Causal AI Future SCORM12 SCORM package