Causality and Statistics, a Love Not So Impossible After All
summary
Introduces basics of causal inference, explaining how it differs from mere correlation using examples and intuition (e.g., spurious correlations). Covers methods combining statistical reasoning and machine‑learning tools, and shows a practical application where causal inference was used to prioritize internal projects based on customer feedback.
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chapters (11)
- 0:00Introduction
- 0:22Causality origins
- 3:55Scientific method limits
- 7:46Causality graph theory
- 11:19Pearl's rules and identifiability
- 15:34Machine learning for estimation
- 18:45Practical graph exercises
- 23:20Real client case
- 27:52Building causal graph
- 31:22Summary and tools
- 32:04Q&A
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