Instructor: Itamar Caspi
Teaching assistant: Dor Goldenberg
🗓️ Spring semester, 2020 ⏰ 17:30 - 20:15 🏨 There's no palce like home... ✍️ github.com/ml4econ
This course covers topics that range between data science, machine learning, and econometrics. In particular, it introduces concepts from the world of ML that can potentially contribute to empirical economics. The course exposes the students to popular supervised and unsupervised machine learning methods, with an emphasis on the challenges and opportunities of integrating these methods in empirical economics, and the relevance of ML to policy analysis and causal inference. The various topics are illustrated through applications, reading empirical articles, and doing applied work.
Course participants will learn to:
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Apply best-practices for data science in the context of empirical research in economics.
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Develop an in-depth and practical knowledge of the challenges and opportunities that arise in applied empirical work that involves high dimensional data.
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Integrate techniques and insights from the world of machine learning into applied empirical research in economics.
Course participants are expected to:
- Have R, RStudio, and Git installed on their own computers.
- Sing up for a (free) DataCamp, GitHub, Rstudio Cloud, and Kaggle accounts.
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 | Topic |
|---|---|
| 1 | Course Overview & Reproducibility |
| 2 | Basic ML Concepts |
| 3 | Regression and Regularization |
| 4 | Classification |
| 5 | Trees and Forests |
| 6 | Causal inference |
| 7 | High-Dimensional Counfounding Adjustment |
| 8 | High-Dimensional Heterogeneous Treatment Effects |
| 9 | Prediction Policy Problems |
| 10 | Unsupervised Learning |
| 11 | Text Analysis |
| 12 | TBA |
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Regression and Regularization
5.1 Prepare browser data
5.2 Ridge and lasso simulation
5.3 Ridge, lasso, PCR and PLS: A Tidymodels Workflow -
Classification (PDF)
6.1Classification: A Tidymodels workflow
Can be found here.
Can be found here.
We will provide course participants free access to DataCamp, the most intuitive learning platform for data science and analytics. Learn any time, anywhere and become an expert in R, Python, SQL, and more. DataCamp’s learn-by-doing methodology combines short expert videos and hands-on-the-keyboard exercises to help learners retain knowledge. DataCamp offers 325+ courses by expert instructors on topics such as importing data, data visualization, and machine learning. They’re constantly expanding their curriculum to keep up with the latest technology trends and to provide the best learning experience for all skill levels. Join over 5 million learners around the world and close your skills gap.
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Itamar Caspi is a Senior Economist and Head of the Monetary Analysis Unit at Bank of Israel's Research Department. In the past, he was a Central Bank Research Fellow at the Bank for International Settlements (BIS) and held a position at the Chief Economist Department of the Israeli Ministry of Finance. He has coauthored several R packages. Itamar received his B.A. degree in economics and business administration from Ben-Gurion University, Beer Sheva, Israel, and an M.A. degree in economics from the Hebrew University of Jerusalem, Jerusalem, Israel. He is expected to receive his Ph.D. degree in Economics from Bar-Ilan University in 2020.
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Dor Goldenberg is as an asistant economist at the Bank of Israel's Research Department. Formerly, he worked at the Israel Competition Authority, where he specialised in health, industry, and military industries, among other fields. His focus is mainly on data science, machine learning and big data. He is currently studying for his MA in economics at the Hebrew University of Jerusalem. In addition, he is a T.A in several courses. He has earned a B.A in Philosophy, Political Science, and Economics from the Hebrew University.
