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lectures.Rmd
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---
title: "Lectures"
---
The schedule below is tentative and subject to change, depending on time and class interests.
We will move at a pace dictated by class discussions. Please check this page often for updates.
| Week | Who | Topic |
|:----------------------|:--------------------|:---------------------------------------------------------------------------------|
| [**1**](#week-1) | Itamar | Course Overview & Basic ML Concepts |
| [**2**](#week-2) | Itamar | Reproducibiliy and Version Control |
| [**3**](#week-3) | Ariel | Regression and K Nearest Neighbors|
| [**4**](#week-4) | Ariel | Classification |
| [**5**](#week-5) | Ariel | Dimensionality Reduction |
| [**6**](#week-6) | Ariel | Decision Trees and Random Forests|
| [**7**](#week-7) | Ariel | Unsupervised Learning|
| [**8**](#week-8) | Itamar | Prediction in Aid of Estimation I - Lasso and Average Treatment Effects |
| [**9**](#week-9) | Itamar | Prediction in Aid of Estimation II - Trees and Heterogeneous Treatment Effects |
| [**10**](#week-10) | Itamar | Prediction Policy Problems |
| [**11**](#week-11) | Itamar | Text Analysis |
*_Note_: Ariels lectures (weeks3-7) are only available on Moodle.
## Week 1
Notes:
- [Course Overview](https://raw.githack.com/ml4econ/notes-spring2019/master/01-overview/01-overview.html)
- [Basic ML Concepts](https://raw.githack.com/ml4econ/notes-spring2019/master/02-basic-ml-concepts/02-basic-ml-concepts.html)
#### Selected references
Breiman, L. (2001). [Statistical modeling: The two cultures](https://projecteuclid.org/euclid.ss/1009213726). _Statistical Science_, 16(3), 199-231.
Athey, S. (2018). [The impact of machine learning on economics](https://www.nber.org/chapters/c14009.pdf). In _The Economics of Artificial Intelligence: An Agenda_. University of Chicago Press.
Mullainathan, S., & Spiess, J. (2017). [Machine learning: an applied econometric approach](https://www.aeaweb.org/articles?id=10.1257/jep.31.2.87). _Journal of Economic Perspectives_, 31(2), 87-106.
## Week 2
Notes: [Reproducibiliy and Version Control](https://raw.githack.com/ml4econ/notes-spring2019/master/03-reprod-vc/03-reprod-vc.html)
[DataCamp in-class](https://www.datacamp.com/enterprise/machine-learning-for-economists):
- Introduction to R
- Working with the RStudio IDE (Part I)
#### Selected references
R and Tidyverse
- [R for Data Science (r4ds)](http://r4ds.had.co.nz/) by Garrett Grolemund and Hadley Wickham.
- [Data wrangling and tidying with the “Tidyverse”](https://raw.githack.com/uo-ec607/lectures/master/05-tidyverse/05-tidyverse.html) by Grant McDerrmot.
- [Getting used to R, RStudio, and R Markdown](https://rbasics.netlify.com/index.html) by Chester Ismay and Patrick C. Kennedy.
- [Data Visualiztion: A practical introduction](https://socviz.co/) by Kieran Healy.
Version Control
- [Happy Git and GitHub for the useR](https://happygitwithr.com/) by Jenny Bryan.
- [Version Control with Git(Hub)](https://raw.githack.com/uo-ec607/lectures/master/02-git/02-Git.html) by Grant McDerrmot.
- [Pro Git](https://git-scm.com/book/en/v2).
## Week 8
Notes: [ML in Aid of Estimation, Part I: Lasso and ATE ](https://raw.githack.com/ml4econ/notes-spring2019/master/08-lasso-ate/08-lasso-ate.html)
#### Selected references
Ahrens, A., Hansen, C. B., & Schaffer, M. E. (2019). lassopack: Model selection and prediction with regularized regression in Stata.
Belloni, A., D. Chen, V. Chernozhukov, and C. Hansen. 2012. Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain. _Econometrica_ 80(6): 2369–2429.
Belloni, A., & Chernozhukov, V. (2013). Least squares after model selection in high-dimensional sparse models. _Bernoulli_, 19(2), 521–547.
Belloni, A., Chernozhukov, V., & Hansen, C. (2013). Inference on treatment effects after selection among high-dimensional controls. _Review of Economic Studies_, 81(2), 608–650.
Belloni, A., Chernozhukov, V., & Hansen, C. (2014). High-Dimensional Methods and Inference on Structural and Treatment Effects. _Journal of Economic Perspectives_, 28(2), 29–50.
Chernozhukov, V., Hansen, C., & Spindler, M. (2015). Post-selection and post-regularization inference in linear models with many controls and instruments. _American Economic Review_, 105(5), 486–490.
Chernozhukov, V., Hansen, C., & Spindler, M. (2016). hdm: High-Dimensional Metrics. _The R Journal_, 8(2), 185–199.
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., & Newey, W. (2017). Double/debiased/Neyman machine learning of treatment effects. _American Economic Review_, 107(5), 261–265.
Mullainathan, S. & Spiess, J., 2017. Machine Learning: An Applied Econometric Approach. _Journal of Economic Perspectives_, 31(2), pp.87–106.
Van de Geer, S. A., & Bühlmann, P. (2009). On the conditions used to prove oracle results for the lasso. _Electronic Journal of Statistics_, 3, 1360–1392.
Zhao, P., & Yu, B. (2006). On Model Selection Consistency of Lasso. _Journal of Machine Learning Research_, 7, 2541–2563.
## Week 9
Notes: [ML in Aid of Estimation, Part II: Trees and CATE ](https://raw.githack.com/ml4econ/notes-spring2019/master/09-trees-cate/09-trees-cate.html)
#### Selected references
Athey, S., & Imbens, G. (2016). Recursive partitioning for heterogeneous causal effects. _Proceedings of the National Academy of Sciences_, 113(27), 7353-7360.
Athey, S., Imbens, G. W., Kong, Y., & Ramachandra, V. (2016). An Introduction to Recursive Partitioning for Heterogeneous Causal Effects Estimation Using `causalTree` package. 1–15.
Davis, J.M. V & Heller, S.B., 2017. Using Causal Forests to Predict Treatment Heterogeneity : An Application to Summer Jobs. _American Economic Review: Papers & Proceedings_, 107(5), pp.546–550.
Lundberg, I., 2017. Causal forests: A tutorial in high-dimensional causal inference. [https://scholar.princeton.edu/sites/default/files/bstewart/files/lundberg_methods_tutorial_reading_group_version.pdf](https://scholar.princeton.edu/sites/default/files/bstewart/files/lundberg_methods_tutorial_reading_group_version.pdf)
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.
## Week 10
Notes: [Prediction Policy Problems ](https://raw.githack.com/ml4econ/notes-spring2019/master/10-pred-policy/10-pred-policy.html)
#### Selected references
Angwin, Julia, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. “Machine Bias.” _ProPublica_, May 23. [https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.](https://www.propublica.org/article/machine-bias-risk-assesments-in-criminal-sentencing.)
Athey, S. (2018). The Impact of Machine Learning on Economics.
Athey, S., & Wager, S. (2018). Efficient Policy Learning.
Kleinberg, B. J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction Policy Problems. _American Economic Review: Papers & Proceedings_, 105(5), 491–495.
Kleinberg, B. J., Ludwig, J., Mullainathan, S., & Rambachan, A. (2018). Algorithmic Fairness. _American Economic Review: Papers & Proceedings_, 108, 22–27.
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018). Human Descisions and Machine Predictions. _Quarterly Journal of Economics_, 133(1), 237–293.
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017). Inherent Trade-Offs in the Fair Determination of Risk Scores. _Proceedings of the 8th Conference on Innovation in Theoretical Computer Science_, 43, 1–23.
Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. _Journal of Economic Perspectives_, 31(2), 87–106.
## Week 11
Notes: [Text Mining](https://raw.githack.com/ml4econ/notes-spring2019/master/11-text-mining/11-text-mining.html)
Hands-on: [Bank of Israel Minutes Text Analysis](https://raw.githack.com/ml4econ/notes-spring2019/master/11-hands-on-textmining/11-hands-on-textmining.html)
#### Selected references
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. _Journal of Machine Learning Research_, 3(Jan), 993-1022.
Blei, D. M., & Lafferty, J. D. (2006, June). Dynamic topic models. In _Proceedings of the 23rd international conference on Machine learning_ (pp. 113-120). ACM.
Gentzkow, M., Kelly, B.T. and Taddy, M. (forthcoming). _The Quarterly Journal of Economics_.
Hansen, S., McMahon, M., & Prat, A. (2017). Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach. _The Quarterly Journal of Economics_, 133(2), 801–870.
Lafferty, J. D., & Blei, D. M. (2006). Correlated topic models. In _Advances in neural information processing systems_ (pp. 147-154).
Loughran, T. and McDonald, B., 2011. When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. _The Journal of Finance_, 66(1), pp.35-65.
Roberts, M.E., Stewart, B.M., Tingley, D., Lucas, C., Leder‐Luis, J., Gadarian, S.K., Albertson, B. and Rand, D.G., 2014. Structural topic models for open‐ended survey responses. _American Journal of Political Science_, 58(4), pp.1064-1082.
Roberts, M.E., Stewart, B.M. and Tingley, D., 2014. stm: R package for structural topic models. _Journal of Statistical Software_, 10(2), pp.1-40.
## Assignments
[Kaggle Competition](https://raw.githack.com/ml4econ/notes-spring2019/master/a-kaggle/a-kaggle.html)