Regression and Matching | Causal Inference in Data Science Part 1

In this video, I have invited my friend Yuan for a mini course on application of Causal Inference in tech companies. This is going to be a video series. In this video, we are going to focus on the methods of Regression and Matching. 📃Yuan's blog post on causal inference https://www.yuan-meng.com/posts/causality/ 📚References recommended by Yuan - Huntington-Klein, N. (2021). The effect. Routledge. - Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27-42. - Taylor, S. (2021, July 15). When do we actually need causal inference?. Lander Analytics. https://youtu.be/2dv7NrYExzo - A/B Testing Pitfalls https://youtu.be/dLwH1kp03kE 🟢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 Topic Of Video 01:10 Why Learn Casual Inference 08:16 Regression 09:58 Pitfalls in Regression 16:16 Matching 18:12 Propensity Score Matching