Machine Learning for Economists, February 2019

These are materials for a machine learning course for the Poverty Global Practice at the World Bank, February 27. If you have any questions or comments please email me at hersh [at] chapman [dot] edu

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February 27, 2010

(9am – 12:15; 2:00 – 5pm)

1. Introduction and Cross-validation
   • [Slides]; [code]; [Introduction to Statistical Learning: Chp 2 & 5.1]
2. Shrinkage methods (Lasso and Ridge)
   • [Slides]; [code]; [ISLR: Chp 6]
3. Classification
   • [Slides]; [code]; [APM: Chp 11, 12 & 16, ISLR: Chp 4]
4. Tree-based methods (Decision trees, bagging, random forests, boosting)
   • [Slides]; [code]; [ISLR: Chp 8]
5. Unsupervised learning (PCA, clustering)
   • [Slides]; [code]; [ISLR: Chp 10]
6. Caret Package (Automated Machine Learning Package)
   • [Slides]; [code]; [APM]

Schedule:
9:00 – 10:30: Lecture
10:30 – 11:00: Coffee break
11:00 – 12:15: Lecture
12:15 – 2:00: Lunch break
2:00 – 3:00: Lecture
3:00 – 3:30: Coffee break
3:30 – 5:00: Lecture

References:

Main texts:

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R(Vol. 103). Springer Science & Business Media.
• Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer Science & Business Media.
• Colin Cameron’s notes on Statistical Learning.

Papers:

• Afzal, M., Hersh, J., Newhouse, D. “Building a better model: Variable selection to predict poverty in Pakistan and Sri Lanka” (2015). Working Paper
• Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483-485.
• Athey, S., Imbens, GW. “Machine Learning Methods for Causal effects”.  http://www.nasonline.org/programs/sackler-colloquia/documents/athey.pdf
• Monica Andini, Emanuele Ciani, Guido de Blasio, Alessio D’Ignazio. “Effective policy targeting with machine learning“
• Belloni, A., & Chernozhukov, V. (2009). Least squares after model selection in high-dimensional sparse models.
• Celiku, B., & Kraay, A. (2017). Predicting conflict. The World Bank
• Blumenstock, J. E. (2016). Fighting poverty with data. Science, 353(6301), 753-754.
• Diamond, Alexis; Gill, Michael; Rebolledo Dellepiane, Miguel Angel; Skoufias, Emmanuel; Vinha, Katja; Xu, Yiqing. 2016. Estimating poverty rates in target populations : an assessment of the simple poverty scorecard and alternative approaches. Policy Research working paper; no. WPS 7793. Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/801751471268674333/Estimating-poverty-rates-in-target-populations-an-assessment-of-the-simple-poverty-scorecard-and-alternative-approaches
• Einav, L., & Levin, J. (2014). Economics in the age of big data. Science, 346(6210), 1243089.
• Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794.
• Engstrom, R., Hersh, J., & Newhouse, D. (2016). Poverty from Space: Using high resolution satellite imagery for estimating economic well-being.
• Hersh, J., & Harding, M. (2018). Big Data in economics. IZA World of Labor, 451-451.
• Harding, Matthew & Lovenheim, Michael, 2017. “The effect of prices on nutrition: Comparing the impact of product- and nutrient-specific taxes,” Journal of Health Economics, Elsevier, vol. 53(C), pages 53-71.
• Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction policy problems. The American economic review, 105(5), 491-495.
• McBride, L., & Nichols, A. (2016). Retooling poverty targeting using out-of-sample validation and machine learning. The World Bank Economic Review, 32(3), 531-550.
• Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106.
• Wager, S., & Athey, S. (2017). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, (just-accepted).