Machine learning has become an increasingly integral part of public policies. It is applied for policy problems that do not require causal inference but instead require predictive inference. Solving these prediction policy problems requires tools that are tuned to minimizing prediction errors, but also frameworks to ensure that models are efficient and fair. EMX+ will introduce the theory and applications of machine learning algorithms with a focus on policy applications and issues. The goals of this course include:
- Developing a basic understanding of the statistical theory underlying common supervised machine learning algorithms
- Developing skills necessary to train and assess the performance of selected popular machine learning algorithms for solving public policy problems
- Gaining an understanding of the benefits and risks of applying machine learning algorithms to public policy problems
The course consists of 6 lessons each consisting of a technical introductory lecture and a hands-on application of the topics to a real-world policy problem. Students will be working with the programming language R (and optionally, Python), but coding is not the primary focus of the course.
Course textbook (ebook available for free): James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: With Applications in R.