Causal Machine Learning Teaching Slides

As part of an advanced undergraduate independent study, I developed a set of teaching slides on Causal Machine Learning. These slides are designed to complement courses such as UNC’s ECON 573: Machine Learning and Econometrics or to support a seminar dedicated to causal ML.

The material is based on the CausalML Book (Chernozhukov et al., 2024), with slide titles corresponding directly to chapters in the text. The slides provide a more accessible introduction to select chapters, making complex methods approachable for advanced undergraduates and graduate students.

Together, these slides provide a structured, digestible entry point into Causal Machine Learning, with emphasis on moving beyond average treatment effects (ATE) toward heterogeneous and conditional effects (HTEs, CATE). They are suitable for instructors, students, and practitioners seeking to apply modern ML techniques to robust causal inference.

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