Causal Effects via Propensity Scores | Introduction & Python Code

🤝 Work with me: https://aibuilder.academy/yt/dm-BWjyYQpw 🚀 Ship AI apps in weeks, not months: https://aibuilder.academy/courses/yt/dm-BWjyYQpw This is the 2nd video in a series on causal effects. Here I introduce the Propensity Score and discuss 3 ways we can use it to compute causal effects from observational data. At the end, I share a concrete example with code of what using these methods might look like in practice. 👉 Series Playlist: https://www.youtube.com/playlist?list=PLz-ep5RbHosVVTz9HEzpI4d6xpWsc8rOa 📰 Read more: https://medium.com/towards-data-science/propensity-score-5c29c480130c?sk=45f0ec6803eba962c0d2d0162185741d 💻 Example Code: https://github.com/ShawhinT/YouTube-Blog/tree/main/causality/propensity_score Resources: - An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies by Peter C. Austin - Data from UCI MLR: https://archive.ics.uci.edu/ml/datasets/census+income Introduction - 0:00 Observational vs Interventional Studies - 0:32 Propensity Score - 3:25 3 Propensity Score-based Methods - 4:56 1) Matching - 5:18 2) Stratification - 9:07 3) Inverse Probability of Treatment Weighting - 10:37 Example: ATE of Grad on Income - 12:29 Word of Caution - 15:46