Causal Effects | An introduction

🤝 Want your team maximizing Claude? I run 1:1 and team AI workshops for companies doing $1M+ per year: https://aibuilder.academy/yt/BOPOX_mTS0g This is the first video in a series on causal effects. Here I introduce the Potential Outcomes Framework and use it to formulate 3 different types of causal effects. In future videos, I discuss how to compute causal effects from observational data. Series Playlist: https://www.youtube.com/playlist?list=PLz-ep5RbHosVVTz9HEzpI4d6xpWsc8rOa 📰 Read more: https://medium.com/towards-data-science/causal-effects-f30f962ffff3?sk=0427e637974652438bc1fb8cf4c2b981 Resources: - An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies by Peter C. Austin - Counterfactuals and Causal Inference: Methods and Principles for Social Research by Stephen L. Morgan & Christopher Winship - An Introduction to Causal Inference by Judea Pearl Introduction - 0:00 Causal Effects - 0:24 3 Types of Variables - 1:01 Potential Outcomes Framework - 1:50 3 Types of Causal Effects - 2:28 1) Individual Treatment Effect (ITE) - 2:38 2) Average Treatment Effect (ATE) - 4:01 2.1) ATE in RCTs - 5:24 3) Average Treatment Effect of Treated/Controls (ATT/ATC) - 6:56 Practical Questions - 9:07