Causality: An Introduction | How (naive) statistics can fail us

🤝 Want your team maximizing Claude? I run 1:1 and team AI workshops for companies doing $1M+ per year: https://aibuilder.academy/yt/WqASiuM4a-A The first video in a 3-part series on causality. This series is based on the work of Judea Pearl, who laid much of the groundwork for this "new science of cause and effect". Future posts will look more closely at two fields of causality: causal inference and causal discovery. Series Playlist: https://www.youtube.com/playlist?list=PLz-ep5RbHosVVTz9HEzpI4d6xpWsc8rOa 📰 Read more: https://medium.com/towards-data-science/causality-an-introduction-f8a3f6ac4c4a?sk=970d8785697588735e3cb3dd7bbf8cf9 Resources: - The Book of Why by Judea Pearl: https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X - Spurious Correlation Examples: https://tylervigen.com/spurious-correlations Introduction - 0:00 Why? - 0:50 3 Traps of Statistics - 1:35 Trap 1: Spurious Correlation - 1:42 Trap 2: Simpson's Paradox - 2:34 Trap 3: Symmetry - 4:09 Defining Causality - 5:32 Representing Causality - 6:24 Closing remarks - 8:01