These are materials for a machine learning course for the Poverty Global Practice at the World Bank, July 12-13. If you have any questions or comments please email me at hersh [at] chapman [dot] edu
July 12, 2018
July 12th (9am – 12:15; 2:00 – 5pm)
- Introduction and Cross-validation
- Shrinkage methods (Lasso and Ridge)
- Tree-based methods (Decision trees, bagging, random forests, boosting)
- Unsupervised learning (PCA, clustering)
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
9am – noon: Coding examples of material on day 1
1:30 – 2:30: Aart Kraay, “Predicting Conflict” (joint with Bledi Celiku)
2:30 – 3:00: Kristen Himelein, “TBD”
3:00 – 3:20: Break
3:20 – 4:20: Leonardo Lucchetti “What can we (machine) learn about welfare dynamics from cross-sectional data?”
4:20 – 5:00: Jonathan Hersh, “Asking the Right Question: Survey Design Using Group Lasso and Sparse Group Lasso” (joint with Leonardo Lucchetti and Nancy Nugent); “Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico” (joint with Boris Babenko, David Newhouse, Anusha Ramakrishnan, Tom Swartz)
- 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 onStatistical Learning.
- 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
- 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.
- 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. (2015). Improved poverty targeting through machine learning: An application to the USAID Poverty Assessment Tools. com/wp-content/uploads/2015/01/improvedtargeting_21jan2015. pdf, retrieved, 4.
- 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).