Experiments, Regression & Causal Analysis for Predictive Modeling and Policy Evaluation
Sub Category
- Economics
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Objectives
- Fundamentals of linear regression and ordinary least squares (OLS) estimation
- How to implement regression models in R
- Techniques to handle confounding variables and unobserved heterogeneity
- Predictive modeling using machine learning: regression trees, random forests, and cross-validation
- Deep dive into causal inference: endogeneity, instrumental variables, and treatment effects
- Design and analysis of controlled experiments and difference-in-differences (DiD)
- Application of instrumental variable estimation and inverse probability weighting
- Real-world case studies including job counseling experiments and search engine marketing
Pre Requisites
- Basic understanding of statistics and data analysis
- Familiarity with R is helpful
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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