Causal Effects via Regression w/ Python Code

🤝 Want your team maximizing Claude? I run 1:1 and team AI workshops for companies doing $1M+ per year: https://aibuilder.academy/yt/O72uByJlnMw This is the 5th video in a series on causal effects. In the previous videos, we discussed different ways to compute treatment effects from data. 👉 Series Playlist: https://www.youtube.com/playlist?list=PLz-ep5RbHosVVTz9HEzpI4d6xpWsc8rOa 📰 Read More: https://medium.com/towards-data-science/causal-effects-via-regression-28cb58a2fffc 💻 Example Code: https://github.com/ShawhinT/YouTube-Blog/tree/main/causality/causal_effects_regression More resources: [1] Causal inference using regression on the treatment variable by Andrew Gelman and Jennifer Hill - http://www.stat.columbia.edu/~gelman/arm/chap9.pdf [2] Double/Debiased Machine Learning for Treatment and Causal Parameters by Victor Chernozhukov et al. - https://arxiv.org/abs/1608.00060 [3] DoubleML Python library: https://docs.doubleml.org/stable/guide/basics.html [4] Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning by Kunzel et al. - https://arxiv.org/abs/1706.03461 [Data] Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. (CC BY 4.0) Intro - 0:00 What is regression? - 0:25 3 Regression-based Techniques - 2:26 1) Linear Regression - 2:47 2) Double Machine Learning - 5:26 3) Metalearners - 9:02 3.1) T-learner - 9:29 3.2) S-learner - 11:24 3.3) X-learner - 12:56 Example Code - 15:12