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Machine Learning for Economists (57750)

Spring 2025 | Hebrew University of Jerusalem

Instructor: Itamar Caspi
Teaching Assistant: Inbar Avni

📅 Spring Semester, 2025
🕒 16:30 - 19:15
🏢 Social Sciences Building, Room 22205
📝 Course Materials
💬 Discussion Forum

Overview

This course integrates data science, machine learning, and econometrics to equip students with fundamental machine learning concepts that can enhance empirical economics research. Students will explore both supervised and unsupervised machine learning methods, with emphasis on their applications in empirical economics. The course highlights the relevance of machine learning to policy analysis and causal inference through real-world applications, empirical research papers, and hands-on assignments.

Learning Objectives

By the end of this course, students will be able to:

  1. Implement data science best practices within empirical economics research
  2. Navigate the challenges and opportunities of working with high-dimensional data in economics
  3. Integrate machine learning techniques into applied economic research

Prerequisites

Students are expected to:

  1. Have their own computers with R, RStudio (Posit), Git, and GitHub Desktop installed
  2. Create free accounts on GitHub and Kaggle

Course Schedule

Note: This schedule may be adjusted based on class interests and time constraints. Please check the course page regularly for updates.

Week Topic
1 Course Overview
2 Basic ML Concepts
3 Reproducibility & ML Workflow
4 Regression and Regularization
5 Classification
6 Trees and Forests
7 Causal Inference
8 High-Dimensional Confounding Adjustment
9 High-Dimensional Heterogeneous Treatment Effects
10 Text Analysis
11 Large Language Models

Course Materials

Part I: Machine Learning

  1. Course Overview
    HTML | PDF

  2. Basic Machine Learning Concepts
    HTML | PDF

  3. Reproducibility

  4. ML Workflow

  5. Regression and Regularization

  6. Classification

  7. Decision Trees and Random Forests

Part II: Causal Inference and ML

  1. Causal Inference

  2. High-Dimensional Confounding Adjustment

  3. High-Dimensional Heterogeneous Treatment Effects

Part III: Unsupervised Learning and Language Models

  1. Text as Data

  2. Large Language Models

Projects

  • Kaggle Competition

Reading Materials

A comprehensive reading list can be found here.

About the Instructor

Itamar Caspi heads the Monetary Analysis Unit at the Bank of Israel and is an adjunct lecturer at Hebrew University. His research focuses on macroeconomics, monetary economics, and applied econometrics. After starting at the Ministry of Finance in 2010, he joined the Bank of Israel in 2012, later serving as a Research Fellow at the Bank for International Settlements. He holds degrees from Ben-Gurion University (BA), Hebrew University/Tel-Aviv University (MA), Harvard Kennedy School (MPA), and Bar-Ilan University (PhD).

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