Teaching

Graduate Econometric Methods (2017-2020)

This is a class on econometric theory, designed for graduates in economics and statistics of the Humboldt University of Berlin. Students learn from Hayashi's Econometrics textbook. Download teaching evaluations 2019/20, 2018/19, 2017/18.

PhD Machine Learning Mini-Course (2018-2019)

This is a yearly PhD-level mini-course in machine learning methods for prediction and treatment effect estimation, developed for students of the Technical University of Berlin. Download syllabus and teaching evaluations.

PhD Machine Learning Online Skill Camp (2020)

This is a PhD-level one week course developed for researchers of the Berlin Network of Labor Market Research (BeNA). The course covers machine learning methods used by economists to solve problems that standard econometrics cannot. This includes supervised learning such as regularized regression and tree-based methods for both prediction and causal effect estimation. Students learn applications in R and familiarize with the algorithms' implementation. More information is available on the course website. Download syllabus and teaching evaluations.

Graduate Machine Learning Lecture (2020)

This is an introductory lecture on "Machine Learning for Economists: in Theory and Practice" for graduate students of the Toulouse School of Economics. Students become familiar with the main concepts and types of machine learning, and learn the potential of these methods for prediction and classification tasks as well as for policy evaluation. The lecture is followed by a Q&A session.

PhD Machine Learning Methods Mini-Course (2020)

This is a PhD-level mini-course in machine learning methods for high-dimensional data in economics, developed for the DIW Berlin Graduate Center and directed to students of all universities. I introduce recent developments in algorithmic statistics used in various fields such as agriculture and artificial intelligence (e.g. self-driving cars). I draw contrasts with traditional approaches (OLS), and I discuss the curse and blessing of high-dimensional data. I introduce the core concepts of machine learning, and analyze both regression- and tree-based methods. Finally, the course focuses on state-of-the-art techniques for handling causal problems in high-dimensions. Download syllabus and visit the course website.

Graduate Quantitative Methods: Machine Learning (2021-2024)

This is a 30-hour course on econometric methods and machine learning, designed for graduates in economic policy and quantitative methods of the Potsdam University and PhD students of the Berlin School of Economics. Starting from the basics of econometrics and OLS, the first part of the course introduces students to high-dimensional predictive problems. In the second part, I build on the standard causal inference literature in econometrics, and show how to handle treatment effect estimation in high-dimensional problems. Students learn R and the most important functions that enable empirical analysis in high-dimensions. Download syllabus and teaching evaluations 2021, 2022, 2023.

Graduate Data Analysis Methods in Environmental Economics (2022-2024)

This course provides a broad introduction to microeconometric empirical methods for grad students of the University of Innsbruck, including traditional econometric methods and machine learning techniques. The target audience are master students interested in learning how to perform data analysis, outcome prediction and policy evaluation. Students will learn how to use the statistical software R. Applications will focus on environmental policies for climate change mitigation (waste and air pollution control). Completing the course will enable students to conduct independent empirical research in their master thesis as well as future jobs (e.g. public policy institutions, consulting firms, and doctoral programs). Download syllabus and teaching evaluations 2022, 2023.

Undergraduate Introduction to Microeconomics/Grundlagen der VWL (2022-2024, in German)

This course provides a broad introduction to Microeconomics for undergrad students of the University of Innsbruck. Download syllabus and teaching evaluations 2022 (I) (II), 2023

Machine Learning for Prediction and Causal Analysis (2023-2024)

The course, designed for grad and undergrad students of the Digital Science Center and Faculty of Economics and Statistics in Innsbruck, provides an understanding of the foundations, scope and approaches of machine learning for prediction and causal analysis and it focuses on their application to problems in social sciences and economics. Starting from the basics of linear regression, which underlies many machine learning models, this course introduces students to high-dimensional predictive and causal problems. In particular, this course provides students with the basic ideas and intuition behind modern machine learning methods as well as an understanding of how, why, and when they work in practice. Download syllabus and teaching evaluations 2023 (I), (II)

PhD Workshop on Machine Learning for Prediction and Causal Analysis (July 2024)

This three-day online workshop, hosted by the University of Huelva, is offered to PhD students interested in machine learning methods for prediction and causal inference. Download syllabus.

Summer School on Machine Learning for Prediction and Causal Analysis (July 2024)

This four-day in-person summer school, hosted at the Lisbon School of Economics and Management, is offered to PhD and graduate students around the world interested in machine learning methods for prediction and causal inference. Visit the website of the ISEG summer school for syllabus and course description.