Week 6: Prediction Policy Problems

Guiding Questions

This week we discuss: What is the role of machine learning and big data in a world of evidence-informed public policy? What are “prediction policy problems” and (how) do they differ from other policy problems? Are RCTs becoming obsolete?

Required readings

• Kleinberg, Jon and Ludwig, Jens and Mullainathan, Sendhil and Obermeyer, Ziad (2015). Prediction Policy Problems. American Economic Review, 105(5), pp. 491–95.
• Bansak, Kirk and Ferwerda, Jeremy and Hainmueller, Jens and Dillon, Andrea and Hangartner, Dominik and Lawrence, Duncan and Weinstein, Jeremy (2018). Improving Refugee Integration Through Data-Driven Algorithmic Assignment. Science, 359(6373), pp. 325–329.
• Glaeser, Edward L and Kominers, Scott Duke and Luca, Michael and Naik, Nikhil (2018). Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life. Economic Inquiry, 56(1), pp. 114–137.

Further reading

• Bansak, Kirk and Hainmueller, Jens and Hangartner, Dominik and Ferwerda, Jeremy and Paulson, Elisabeth and Delevoye, Angie and Adams-Cohen, Nicholas and Ramaswami, Ashwin and Kurer, Selina and Pianzola, Joelle and Hotard, Michael (2026). AI-based Matching Improves Refugee Employment in a Double-blind Randomized Trial. Unpublished manuscript. arXiv preprint 2609.35448.
• Kleinberg, Jon and Lakkaraju, Himabindu and Leskovec, Jure and Ludwig, Jens and Mullainathan, Sendhil (2018). Human Decisions and Machine Predictions. The Quarterly Journal of Economics, 133(1), pp. 237–293.
• Athey, Susan (2017). Beyond Prediction: Using Big Data for Policy Problems. Science, 355(6324), pp. 483–485.
• Acemoglu, Daron (2021). Harms of AI. National Bureau of Economic Research, Working Paper No. 29247.
• The Behavioural Insights Team (2017). Using Data Science in Policy.

Suggested media

• How a Pioneer of Machine Learning Became One of Its Sharpest Critics, The Atlantic, May 19, 2018.