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. ML4PP will introduce the theory and applications of machine learning algorithms with a focus on policy applications and issues. The goals of this course include:
The course consists of 6 sessions 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, but coding is not the primary focus of the course.
To end the course, we will meet online for a Collaborative Policy Challenge, which will be delivered by a colleague from an International Organisation. In groups of interdisciplinary teams, we will provide a possible solution to the challenge, and get feedback from our peers and policy experts.
Course textbook (e-book available for free):
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning
· Introduction to the course
· Introduction to the R statistical programming language with the Rstudio IDE
· Introduction to the Python programming language with Visual Studio Code
Release Date: 30 November 2023
Instructors: Stephan, Alex and Michelle (who will give you a warm welcome!)
Social hours: Monday, 4 December at 6pm CET / 11am GMT-6. Join us online at The ML4PP Gather Town
· Prediction Policy problems
· Inference vs. prediction for policy analysis
· Assessing accuracy: bias-variance tradeoff
· Training error vs. test error
· Feature selection: brief introduction to Lasso
Instructors: Michelle González Amador
Readings:
Mandatory
An introduction to Statistical learning, Chapter 2, 3 (Regression), 5 (Cross-validation) and 6 (for more about Lasso).
Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483-485.
Kleinberg, J., Ludwig, J., Mullainathan, S. and Obermeyer, Z., 2015. Prediction policy problems. American Economic Review, 105(5), pp.491-95.
Optional readings
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J. and Mullainathan, S., 2017. Human decisions and machine predictions. The Quarterly Journal of Economics, 133(1), pp.237-293.
Hanna, R., & Olken, B. A. (2018). Universal basic incomes versus targeted transfers: Anti-poverty programs in developing countries. Journal of Economic Perspectives, 32(4), 201-26. (exercise application)
McBride, L., & Nichols, A. (2018). Retooling poverty targeting using out-of-sample validation and machine learning. The World Bank Economic Review, 32(3), 531-550.pter 5.1
Release date: 7 December, 2023.
Social hours: Friday, 15 December at 6pm CET / 11am GMT-6. Join us online at The ML4PP Gather Town Please also sign up for a slot via this link.
· Logistic regression
· Confusion matrix
· Performance metrics: Accuracy, Recall, Precision (...)
Release Date: 21 December, 2023.
Instructor: Dr. Stephan Dietrich
Readings:
An introduction to Statistical learning Chapter 4
Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685-725.
McBride, L., & Nichols, A. (2018). Retooling poverty targeting using out-of-sample validation and machine learning. The World Bank Economic Review, 32(3), 531-550.pter 5.1
Optional Readings
Social hours: Monday, January 8, 6pm CET / 11am GMT-6. Join us online at The ML4PP Gather Town Please register for a slot in the following link
· Decision Trees: a classification approach
· Ensemble learning: bagging and boosting.
Release date: 4 January, 2024.
Instructor: Dr. Francisco Rosales
Readings:
Optional Readings
Social hours: Wednesday, January 31, 4pm CET / 9am GMT-6. Join us online at The ML4PP Gather Town Please register for a slot in the following link
· Common Machine Learning algorithms in (public policy) action
· Black box algorithms
· Biases
· Ethical challenges
Readings:
Fast AI: Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD, Chapter 4.
Kasy, M., & Abebe, R. (2021, March). Fairness, equality, and power in algorithmic decision-making. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 576-586).
Fairness and Machine Learning: Limitations and Opportunities, Chapter 4.
Release date: 30 January, 2024.
Instructor: Dr. Juba Ziani
· Neural Network Architecture: neurons and layers
· Inputs and output: the activation function (sigmoid, tahn...)
Release date: 19 February, 2024.
Instructor: Prof. Dr. Robin Cowan
Optional Readings
Copyright © 2022 Michelle González Amador & Stephan Dietrich . All rights reserved.