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 Unpublished manuscript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
• 11.1-11.3 here

Week 38 🔗

• Chapters 3 and 6 from Speech and Language Processing, 2025 by Jurafsky & Martin, !!! edition from January 2025 !!!

• Mikolov et al. (2013) a b

Week 39 🔗

• Chapter 9 from Speech and Language Processing, 2025 by Jurafsky & Martin, !!! edition from January 2025 !!!
• Vaswani et al. (2017)

Week 40 🔗

• Chapter: Large Language Models from Speech and Language Processing, 2025 by Jurafsky & Martin, edition from August 2025
• Chapter: Post-training: Instruction Tuning, Alignment, and Test-Time Compute from Speech and Language Processing, 2025 by Jurafsky & Martin, edition from August 2025
• Ouyang et al. (2022)

Week 41 🔗

• Chapters 7.3, 10.4, 10.5, 11.1, 11.3 from Speech and Language Processing, 2025 by Jurafsky & Martin, edition from August 2025
• Chapter 6 “Prompt Engineering” from Hands-on LLMs by Alammar & Grootendorst
• Kojima et al., 2022
• Blog post on Reasoning
• Blog post on Agentic AI

Policy 🔗

Week 43 🔗

Fairness Definitions & Precision/Recall

• Precision/Recall
• Very brief overview of fairness definitions + 1h video tutorial
• Overview of fairness definitions
• Book on Fairness (optional)
• Mehrabi et al, 2022 (optional)

Case Studies

• Buolamwini & Gebru, 2018
• Cabello et al, 2023 (optional, we won’t discuss this in the lecture)

Theoretical work

• Bell et al., 2023 (revisits the two papers below + 13 min. video)
• Chouldechova, 2017
• Kleinberg et al., 2016

Week 44 and Week 45 🔗

Case Studies (same as session 1)

Prediction policy formalization

• Kleinberg, J., Ludwig, J., Mullainathan, S. and Obermeyer, Z., 2015. Prediction policy problems. American Economic Review, 105(5), pp.491-495.

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 🔗

• Vansteelandt, Stijn. “Statistical Modelling in the Age of Data Science.” Observational Studies 7, no. 1 (2021): 217–28.
• Ahrens, Achim, Victor Chernozhukov, Christian Hansen, Damian Kozbur, Mark Schaffer, and Thomas Wiemann. “An Introduction to Double/Debiased Machine Learning.” arXiv:2504.08324. Preprint, arXiv, April 11, 2025.
• Causal ML Book Chapter 9

For the curious, the original DML paper:

• Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, et al. “Double/Debiased Machine Learning for Treatment and Structural Parameters.” The Econometrics Journal 21, no. 1 (2018): C1–68.

Week 48 🔗

• Causal ML Book chapter 16

Week 49 🔗

Identifying human prediction mistakes Rambachan, A. (2024) ‘Identifying prediction mistakes in observational data’, The Quarterly Journal of Economics, 139(3), pp. 1665–1711.

Policy applications of causal ML Knittel, C.R. and Stolper, S. (2025) ‘Using machine learning to target treatment: The case of household energy use’, The Economic Journal, 135(672), pp. 2377–2401. Goller, D., Lechner, M., Pongratz, T. and Wolff, J. (2025) ‘Active labor market policies for the long-term unemployed: New evidence from causal machine learning’, Labour Economics, 94, Article 102729.

Week 50 (outro) 🔗

No readings

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