Readings

Readings 🔗

General notes 🔗

Introduction 🔗

Foundations 🔗

Week 36 🔗

Papers – Capabilities 🔗

• Mitchell, M., 2023. AI’s challenge of understanding the world. Science, 382(6671), eadm8175.
• Mitchell, M., 2024. The metaphors of artificial intelligence. Science, 386(6723), eadt6140.
• Mitchell, M., 2025. Artificial intelligence learns to reason. Science, 387(6740), eadw5211.

Papers with Case Studies (revisited in Session 8 and 9) 🔗

• Brynjolfsson, E., Li, D. and Raymond, L., 2025. Generative AI at Work. Quarterly Journal of Economics, 140(2), pp.889–942.
• Agarwal, N., Moehring, A., Rajpurkar, P. and Salz, T., 2023. Combining human expertise with artificial intelligence: Experimental evidence from radiology. NBER Working Paper No. 31422.
• Yu, F. et al., 2024. Heterogeneity and predictors of the effects of AI assistance on radiologists. Nature Medicine, 30(3), pp.837-849.
• Grimon, M-P. and Mills, C., 2025. Better Together? A field experiment on human-algorithm interaction in child protection. arXiv preprint arXiv:2502.08501.
• Argyle, L.P., Busby, E.C., Gubler, J.R., et al., 2025. Testing theories of political persuasion using AI. Proceedings of the National Academy of Sciences, 122(18), e2412815122.
• Dubova, M., Chandramouli, S., Gigerenzer, G., Grünwald, P., Holmes, W., Lombrozo, T., Marelli, M., Musslick, S., Nicenboim, B., Ross, L.N., Shiffrin, R., White, M., Wagenmakers, E-J., Bürkner, P-C. and Sloman, S.J., 2025. Is Ockham’s razor losing its edge? New perspectives on the principle of model parsimony. Proceedings of the National Academy of Sciences, 122(5), e2401230121.

Basic and linear machine learning 🔗

• ESL: Chapters 1–4

Inspirational References 🔗

ML to uncover new data sources for social science

• Text data
  • Ash, E., Morelli, M. and Vannoni, M., 2025. More Laws, More Growth? Evidence from US States. Journal of Political Economy, 133(7), pp.2139–2179.
• Images
  • Blumenstock, J., Cadamuro, G. and On, R., 2015. Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), pp.1073–1076.
  • Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E.L. and Fei-Fei, L., 2017. Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Proceedings of the National Academy of Sciences, 114(50), pp.13108–13113.
  • Voth, H-J. anf Yanagizawa-Drott, D., 2024. Image(s). Unpublished manuscript

Week 37 🔗

Week 38 🔗

The lectures for week 38-41 are mainly based on Speech and Language Processing, 2025 by Jurafsky & Martin.

• Mikolov et al. (2013) a b

Week 39 🔗

• Vaswani et al. (2017)

Week 40 🔗

TBD

Week 41 🔗

TBD

Policy 🔗

Week 44 and Week 45 🔗

Case Studies (same as session 1)

Fairness

• Liang, A., Lu, J., Mu, X. and Okumura, K., 2021. Algorithm Design: A Fairness-Accuracy Frontier. arXiv preprint arXiv:2112.09975.
• Auerbach, E., et al., 2024. Testing the Fairness-Accuracy Improvability of Algorithms. arXiv preprint arXiv:2405.04816.
• Kasy, M. and Abebe, R., 2021. Fairness, Equality, and Power in Algorithmic Decision-Making. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT), pp.576–586.
• Arnold, D., Dobbie, W. and Hull, P., 2025. Building Nondiscriminatory Algorithms in Selected Data. American Economic Review: Insights, 7(2), pp.231–249.

Delegation and Learning

• Agarwal, N., Moehring, A. and Wolitzky, A., 2025. Designing Human-AI Collaboration: A Sufficient-Statistic Approach (No. w33949). National Bureau of Economic Research.
• Noti, G., Donahue, K., Kleinberg, J. and Oren, S., 2025. Ai-assisted decision making with human learning. arXiv preprint arXiv:2502.13062.

Econ ML 🔗

Week 46 🔗

Basic econometrics

• Angrist and Pischke (2008): chapters 2 and 3

Causal trees and forests

• Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360.
• Wager, S. and Athey, S., 2018. Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), pp.1228-1242.
• Athey, S., Tibshirani, J. and Wager, S., 2019. Generalized random forests. The Annals of Statistics, 47(2), pp.1148-1178.
• Athey, S. and Wager, S., 2019. Estimating treatment effects with causal forests: An application. Observational studies, 5(2), pp.37-51.

Week 47 🔗

• An Introduction to Double/Debiased Machine Learning
• Causal ML Book (available here)

Week 48 🔗

• TBD

Outro 🔗

Books 🔗

• ESL: Hastie, T., Tibshirani, R. and Friedman, J., 2009. The elements of statistical learning. Spring Series in Statistics

• Angrist, J. D., and Pischke, J. S. (2008). Mostly harmless econometrics: An empiricist’s companion. Princeton university press.

• Causal ML book

Other Useful Ressources 🔗

• Data Science for Economists
• Coding for Economists
• NumEconCopenhagen
• Causal Inference for The Brave and True
• CS224n: Natural Language Processing with Deep Learning