Date/Time: Mondays, 6 - 8 pm @KAP 319
Tentative meeting dates: sign-up here.
9/22/2025 – Organizing Meeting
9/29/2025 – Regular Meeting
10/6/2025 – Regular Meeting
10/13/2025 – Regular Meeting
10/20/2025 – Regular Meeting
10/27/2025 – Regular Meeting
11/3/2025 – Regular Meeting
11/10/2025 – Regular Meeting
11/17/2025 – Regular Meeting
11/24/2025 – No meeting (Thanksgiving week)
12/1/2025 – Regular Meeting
Format: 2 presenters per meeting, 1 hour for each presenter
Presenters will present one of the following (please email me the slides in advance)
(i) One of the papers from the list below;
(ii) Any economics research paper (or reports) of their choice (highly recommended, please consult me before selecting);
(iii) Their own research project in ML and economics
Suggested Structure:
10 mins – Research Question & Background
What is the research question?
Why is this work important?
Brief context and motivation.
20 mins – Overview of the Main ML Method
Explain the key ML technique used in the paper.
Focus on its intuition and how it applies to the research.
Think of this as an opportunity to learn yourself and teach the method to your peers (and me!).
30 mins – Summary of Main Findings & Takeaways
What are the key results?
What are the policy or research implications?
Any limitations or open questions?
Additional Notes:
No deep math required – prioritize intuition and logic over technical derivations.
This is NOT a class. There are no grades, no evaluations, and no reputational concerns. The goal is simply to learn together in a collaborative environment.
If you don’t fully understand something, that’s okay! Present what you do understand, and we’ll discuss and figure it out as a group.
Paper list:
1. "Machine Learning: An Applied Econometric Approach"
Link: https://www.aeaweb.org/articles?id=10.1257/jep.31.2.87
2. "Generalized Random Forests" (Athey, Tibshirani, Wager 2019)
Link: https://projecteuclid.org/journals/annals-of-statistics/volume-47/issue-2/Generalized-random-forests/10.1214/18-AOS1709.full
3. "Deep IV: A Flexible Approach for Counterfactual Prediction" (Hartford et al. 2017)
Link: https://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf
4. "Deep Learning for Economists" (Dell 2025, JEL)
Link: https://www.aeaweb.org/articles?id=10.1257/jel.20241733
5. "The Macroeconomy as a Random Forest" (Goulet Coulombe 2020, working paper)
Link: https://onlinelibrary.wiley.com/doi/full/10.1002/jae.3030
6. "Neural Network Approach to Demand Estimation and Dynamic Pricing in Retail" (Safonov 2024, arXiv)
Link: https://arxiv.org/abs/2412.00920
7. "Combining Satellite Imagery and Machine Learning to Predict Poverty" (Jean et al. 2016, Science)
Link: https://www.science.org/doi/10.1126/science.aaf7894
8. "Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US" (Gebru et al. 2017, PNAS)
Link: https://arxiv.org/abs/1702.06683
9. "Harnessing Graph Neural Networks to Predict International Trade Flows" (Sellami et al. 2024, Big Data and Cognitive Computing)
Link: https://www.mdpi.com/2504-2289/8/6/65
10. "Credit Scores: Performance and Equity" (Albanesi & Vamossy 2024, NBER Working Paper)
Link: https://www.nber.org/papers/w32917
11. "Using Neural Networks to Predict Microspatial Economic Growth" (Khachiyan et al. 2022, AER: Insights)
Link: https://thedocs.worldbank.org/en/doc/ec67bacb84595f64cf0248a2d5e155b0-0050022023/original/using-neural-networks-to-predict-microspatial-economic-growth-aeri.pdf
12. "Machine Learning for Economics Research: When, What, and How?" (Desai 2023, Bank of Canada WP)
Link: https://arxiv.org/pdf/2304.00086.pdf
13. "Macroeconomic Indicator Forecasting with Deep Neural Networks" (Cook & Smalter Hall 2017, Fed. Res. Bank of Kansas City RWP)
Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3046657
14. "Difference-in-Difference Causal Forests, with an Application to Payroll Tax Incidence in Norway" (Gavrilova et al. 2023, CESifo WP)
Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4500857
15. "Text as Data" (Gentzkow, Kelly, Taddy 2019, JEP)
Link: https://www.aeaweb.org/articles?id=10.1257/jel.20181020
16. “Machine Learning for Economic Forecasting: An Application to China’s GDP Growth”
Link: https://arxiv.org/abs/2407.03595v1
17. “Machine Learning for Public Good: Predicting Urban Crime Patterns to Enhance Community Safety”
Link: https://arxiv.org/abs/2409.10838
18. “Student Performance Prediction Using Machine Learning Algorithms”
Link: https://onlinelibrary.wiley.com/doi/full/10.1155/2024/4067721
19. “The impact of extracurricular education on socioeconomic mobility in Japan: an application of causal machine learning”
Link: https://arxiv.org/abs/2506.07421