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 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
An introduction to Statistical learning, Chapter 2, 3 (Regression), 5 (Cross-validation) and 6 (for more about Lasso).
Kleinberg, J., Ludwig, J., Mullainathan, S. and Obermeyer, Z., 2015. Prediction policy problems. American Economic Review, 105(5), pp.491-95.
Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483-485.
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: Thursday, 14 December at 6pm CET / 11am GMT-6. Join us online at The ML4PP Gather Town
· Logistic regression · Confusion matrix · Performance metrics: Accuracy, Recall, Precision
Release Date: 21 December, 2023.
Instructor: Dr. Stephan Dietrich
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.
Social hours: tbd. Join us online at The ML4PP Gather Town
· Decision Trees · Ensemble learning: bagging and boosting.
Release date: 4 January, 2024.
Instructor: Dr. Francisco Rosales
· The concept of a neuron · Network Layers · Neural Network Architecture
Release date: 25 January, 2024.
Instructor: Dr. Emanuele Pugliese
· An introduction to Statistical Learning, Chapter 3, 5, 6 8 · Indicators · Black box algorithms · Biases · Ethical challenges
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).
Release date: 15 February, 2024.
Instructor: Dr. Juba Ziani
Copyright © 2022 Michelle González Amador & Stephan Dietrich . All rights reserved.