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.
• Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J. and Mullainathan, S., 2018. Human decisions and machine predictions. The quarterly journal of economics, 133(1), pp.237-293.

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 Unpublished manuscript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 🔗

• Michael A. Nielsen, “Neural Networks and Deep Learning”, Determination Press, 2015
  • Available online here
• Chapter 11.1-11.8 in ESLII
• Chapter 9.3 in Causal ML Book
• A Gentle Introduction to torch.autograd

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

Week 48 🔗

• TBD

Outro 🔗

Books 🔗

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

ESLII 🔗

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

CausalMLBook 🔗

• 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