
Causal Inference in Machine Learning: A Practical Guide to Moving Beyond Correlation in Real-World AI Systems
Jonathan Pierce
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Opening Credits
1/11/2026
Chapter 1: From Prediction to Intervention
1/11/2026
Chapter 2: Seeing Causes: Causal Diagrams as a Thinking Tool
1/11/2026
Chapter 3: Confounders, Mediators, and Colliders: Untangling Causal Paths
1/11/2026
Chapter 4: Identification Strategies: When Can We Learn Causal Effects
1/11/2026
Chapter 5: Designing and Interpreting Experiments and A B Tests
1/11/2026
Chapter 6: Working Without Randomization: Matching and Weighting Methods
1/11/2026
Chapter 7: Leveraging Time: Difference in Differences and Synthetic Controls
1/11/2026
Chapter 8: Beyond Averages: Heterogeneous Treatment Effects and Uplift Modeling
1/11/2026
Chapter 9: Causality in the ML Workflow: Time, Feedback, and Bias
1/11/2026
Chapter 10: From Theory to Practice: Tools, Communication, and Project Playbooks
1/11/2026
Closing Credits
1/11/2026