Microcredential ekomex Predictive and Causal Machine Learning

Learn to use R for predictive and causal machine learning, leveraging data to optimally control for observed characteristics, for forecasting outcomes (e.g., sales) and evaluating the impact of treatments/interventions (e.g., training programs)—in two days, online.

What Is This Course About?
This two-day online course introduces PhD students to predictive and causal machine learning using the "R" software. You'll learn to forecast outcomes like sales based on patterns in data and to assess the causal effects of treatments/interventions, such as job training programs, by controlling for confounders in a data-driven manner. The course covers key machine learning algorithms, practical applications for prediction and causal analysis, including effect heterogeneity analysis, and hands-on experience with real-world data using R and R Studio.

Learning Goals

• To understand the ideas and goals of machine learning for prediction and causal analysis
• To understand the intuition, advantages, and disadvantages of alternative methods 
• To understand the similarities and differences between machine learning and classical statistics/econometrics
• To be able to critically assess the application of machine learning in empirical studies
• To be able to apply predictive and causal machine learning to real world data using the software “R” and its interface “R Studio”

Recommended Readings for the Course

• Chernozhukov, V., Hansen, C., Kallus, N., Spindler, M., & Syrgkanis, V. (2024). Applied Causal Inference Powered by ML and AI. arXiv preprint arXiv:2403.02467.
• Huber, M. (2023). Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R, Chapter 5. MIT Press.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: with Applications in R. New York: Springer.

Assignments for the Course
Practical assignment to be solved in R

Schedule

First day 09:30-11:30: Introduction to basic concepts of machine learning
First day 13:30-15:30: Predictive machine learning algorithms 
First day 15:45-16:45: Office hour

Second day 09:30-11:30: Causal machine learning algorithms
Second day 13:30-15:30: Effect heterogeneity analysis and optimal policy learning
Second day 15:45-16:45: Office hours

Who Is Your Instructor?
Martin Huber earned his Ph.D. in Economics and Finance with a specialization in econometrics from the University of St. Gallen in 2010. Following this, he served as an Assistant Professor of Quantitative Methods in Economics at the same institution. He undertook a visiting appointment at Harvard University in 2011–2012 before joining the University of Fribourg as a Professor of Applied Econometrics in 2014. His research encompasses methodological and applied contributions across various fields, including causal analysis and policy evaluation, machine learning, statistics, econometrics, and empirical economics. Martin Huber's work has been published in academic journals such as the Journal of the American Statistical Association, the Journal of the Royal Statistical Society B, the Journal of Econometrics, the Review of Economics and Statistics, the Journal of Business and Economic Statistics, and the Econometrics Journal, among others. He is also the author of the book "Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R."
 

Bluesky: causalhuber.bsky.social

Linkedin: martin-huber-038a172