Causal Inference | Answering causal questions

🤝 Want your team maximizing Claude? I run 1:1 and team AI workshops for companies doing $1M+ per year: https://aibuilder.academy/yt/PFBI-ZfV5rs The second video in a 3-part series on causality. In this video I discuss key ideas from causal inference, which aims at answering question about cause-and-effect. I finish with a concrete example with code of doing causal inference in Python. Series Playlist: https://www.youtube.com/playlist?list=PLz-ep5RbHosVVTz9HEzpI4d6xpWsc8rOa 📰 Read more: https://medium.com/towards-data-science/causal-inference-962ae97cefda?sk=d68d5191fdb00d3fee47aaa43ed48f3d 💻 Example code: https://github.com/ShawhinT/YouTube-Blog/tree/main/causality/causal_inference Resources: - The Book of Why by Judea Pearl: https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X - Do-calculus: https://arxiv.org/abs/1210.4852 - Metalearner paper: https://www.pnas.org/content/116/10/4156 Introduction - 0:00 Causal Inference - 0:28 3 Gifts of Causal Inference - 1:13 Gift 1: Do-operator - 1:20 Gift 2: Confounding (deconfounded) - 3:22 Gift 3: Causal Effects - 5:51 Example: Treatment Effect of Grad School on Income - 8:05 Closing remarks - 11:12