This video is the second part of our mini course on application of Causal Inference in data science. We are going to discuss what kind of methods you can use to do Causal Inference with just a few treated units. Two methods are introduced: difference-in-differences and synthetic control. 🔗 Regression and Matching https://www.youtube.com/watch?v=gUMhBkof7Ck 📃Yuan's blog post on causal inference https://www.yuan-meng.com/posts/causality/ 📚 Resources recommended by Yuan - Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature, 59(2), 391-425. - Jones, N., & Barrows, S. (2019, July 24). Uber’s synthetic control. PyData Amsterdam 2019. https://www.youtube.com/watch?v=j5DoJV5S2Ao - Python/R/Stata code from The Effect: An Introduction to Research Design and Causality: https://github.com/NickCH-K/causaldata - The “synth” package for synthetic control: https://rpubs.com/danilofreire/synth 🟢Get all my free data science interview resources https://www.emmading.com/resources 🟡 Product Case Interview Cheatsheet https://www.emmading.com/product-case-cheat-sheet 🟠 Statistics Interview Cheatsheet https://www.emmading.com/statistics-interview-cheat-sheet 🟣 Behavioral Interview Cheatsheet https://www.emmading.com/behavioral-interview-cheat-sheet 🔵 Data Science Resume Checklist https://www.emmading.com/data-science-resume-checklist ✅ We work with Experienced Data Scientists to help them land their next dream jobs. Apply now: https://www.emmading.com/coaching // Comment Got any questions? Something to add? Write a comment below to chat. // Let's connect on LinkedIn: https://www.linkedin.com/in/emmading001/ ==================== Contents of this video: ==================== 00:00 How to measure COVID's Impact on the Economy 08:13 Difference-in-Differences 14:47 Synthetic Control 24:17 Summary