Week 5: Prediction and Causation
Instructions
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
- The Behavioural Insights Team (2017). Using Data Science in Policy.
- 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.