PhD Course: Causal Machine Learning for Social Scientists
Copenhagen Businesss School, Department of Economics
Causal Machine Learning for Social Scientists
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Machine learning has become an increasingly important tool for causal analysis in the social sciences. This course introduces PhD students and early-career researchers to modern causal machine learning methods that combine the flexibility of machine learning with the rigor of causal inference. The course begins with a brief introduction to key predictive machine learning techniques, including regularized regression and tree-based methods, before turning to methods for handling confounding, estimating differential treatment effects, and learning optimal policies from data.
The course emphasizes both the conceptual foundations and practical implementation of causal machine learning methods through hands-on coding sessions in R and applications from economics, business, and other social sciences. Participants will learn how machine learning can be used to estimate causal effects in high-dimensional settings, uncover treatment effect heterogeneity, and inform evidence-based policy decisions. Throughout the course, the strengths and limitations of these approaches are discussed in relation to traditional econometric methods.
Course Procedure
The course consists of 5 days of classes and will be held physically at the CBS campus. Living costs are at the student’s expense.
Instructor
Anthony Strittmatter (Professor), UniDistance Suisse.
Exam
Participants are required to present a research idea based on the contents of the course. The student presentations will take place on the final day of the course.
ECTS-point
Upon completing all course activities, participants will be awarded 5 ECTS credits and a course certificate.
Course Fees
The course is free of charge.
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