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Machine Learning for Economic Analysis 2020

Material for the course by Damian Kozbur @UZH. The primary reference for introductory machine learning concepts and econometrics is Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, “An Introduction to Statistical Learning with Applications in R”, freely available here. For an advanced analysis of the topics, the recommended book is "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, Jerome Friedman freely available here.

The exercise sessions are entirely coded in Python on Jupyter Notebooks. Recommended free resources are the documentation of the Python library scikit-learn and Bruce Hansen's Econometrics book.


Students should be familiar with the following concepts:

  • Matrix Algebra
    • Econometrics, appendix A.1-A.10
  • Conditional Expectation and Projection
    • Econometrics, chapter 2.1-2.25
  • Large Sample Asymptotics
    • Econometrics, chapter 6.1-6.5
  • Python basics

Exercise Sessions

  1. OLS Regression

    • ISLR, chapter 3
    • ESL, chapter 3
    • Econometrics, chapters 3 and 4
  2. Instrumental variables

    • Econometrics, chapter 12.1-12.12
  3. Nonparametric regression

    • ISLR, chapter 7
    • ESL, chapter 5
    • Econometrics, chapters 19 and 20
  4. Cross-validation

    • ISLR, chapter 5
    • ESL, chapter 7
  5. Lasso and forward regression

    • ISLR, chapter 6
    • ESL, chapters 3 and 18
    • Econometrics, chapter 29.2-29.5
  6. Convexity and optimization

  7. Trees and forests

    • ISLR, chapter 8
    • ESL, chapters 9, 10, 15, 16
    • Econometrics, chapter 29.6-29.9
  8. Neural Networks

    • ESL, chapter 11
  9. Post-double selection

    • Econometrics, chapter 3.18
    • Belloni, Chen, Chernozhukov, Hansen (2012)
    • Belloni, Chernozhukov, Hansen (2014)
    • Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, Robins (2018)
  10. Unsupervised learning

    • ISLR, chapter 10
    • ESL, chapter 14


  • Athey, S., & Imbens, G. W. (n.d.). Machine Learning Methods Economists Should Know About. 62.
  • Belloni, A., Chen, H., Chernozhukov, V., & Hansen, C. B. (2012). Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain. Econometrica, 80(6), 2369–2429.
  • Belloni, A., Chernozhukov, V., & Hansen, C. (2014). Inference on Treatment Effects after Selection among High-Dimensional Controls. The Review of Economic Studies, 81(2), 608–650.
  • Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68.
  • Franks, A., Miller, A., Bornn, L., & Goldsberry, K. (2015). Characterizing the spatial structure of defensive skill in professional basketball. The Annals of Applied Statistics, 9(1), 94–121.
  • Gentzkow, M., Shapiro, J. M., & Taddy, M. (2019). Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech. Econometrica, 87(4), 1307–1340.
  • Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017). Human Decisions and Machine Predictions. The Quarterly Journal of Economics.
  • Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction Policy Problems. American Economic Review, 105(5), 491–495.
  • Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87–106.
  • Wager, S., & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association, 113(523), 1228–1242.


These exercise sessions heavily borrow from


If you have any issue or suggestion for the course, please feel free to pull edits or contact me via mail. All feedback is greatly appreciated!


Material for the exercise sessions of master course Machine Learning for Economic Analysis @uzh





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