Machine Learning for Economists:
ml4econ

Machine Learning for Economists

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🗓️ March, 2020
🕗 17:30 - 20:00
📍 HUJI, Mount Scopus
⚓ ml4econ.rbind.io

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 & Basic ML Concepts
2	Reproducibiliy and Version Control
3	Regression and Classification
4	Regularization
5	Decision Trees and Random Forests
6	Unsupervised Learning
7	Prediction in Aid of Estimation I - Lasso and Average Treatment Effects
8	Prediction in Aid of Estimation II - Trees and Heterogeneous Treatment Effects
9	Prediction Policy Problems
10	Text as Data

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Overview

This course covers topics that range between machine learning (ML) and econometrics. In particular, we will discuss concepts from the world of ML that can potentially contribute to empirical research in economics. The course will cover basic machine learning (supervised and unsupervised) methods, with an emphasis on the challenges and opportunities of integrating these methods in 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.

Pre-work

Welcome to the Machine Learning for Economists course! Before attending class, please complete the following prework:

1. Sign up for a free RStudio Cloud account at https://rstudio.cloud/ before the workshop. I recommend logging in with an existing Google or GitHub account, if you have one (rather than creating a new account with another password you have to remember).

2. We will be using GitHub in this course for version control and publishing. Sign up for a free GitHub.com account at https://github.com/join if you don’t already have one. Also:

   • Complete these installation instructions.

   • Test your connection between GitHub and RStudio following these steps.

   • NOTE: We strongly recommend that if you are not already a fluent GitHub user you choose HTTPS over SSH.

3. Complete this 10-minute interactive tutorial on Markdown.

4. Please bring a laptop that has the following installed:

   • A recent version of R (>=3.6.0), which is available for free at https://cloud.r-project.org/

   • A recent version of RStudio Desktop (>=1.2), available for free (RStudio Desktop Open Source License)

   • The R packages we will use, which you can install by connecting to the internet, opening RStudio, and running at the command line:

     install.packages(c("rmarkdown", "devtools", "usethis", "here", 
                        "tidyverse", "xaringan", "flexdashboard", 
                        "distill", "bookdown", "blogdown",
                        "glue", "wesanderson"))

5. Don’t forget your power cord!

On the day of the course, I’ll provide you with an RStudio Cloud project that contains all of the course materials. We will use the software listed above only as an important backup should there be problems with the on-site internet connection.

View slides (note: these slides are intentionally a loop and will play on autoadvance)

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This work is licensed under a Creative Commons Attribution 4.0 International License.