02. Causal Machine Learning

CATE, uplift modeling, meta-learners, causal forests, Double ML, policy learning, OPE, and validation.

This track focuses on causal questions where modern machine learning is useful after the estimand is clear: heterogeneous treatment effects, nuisance modeling, policy targeting, off-policy evaluation, and validation.

Notebook links open rendered HTML pages generated from the source notebooks under notebooks/lectures/. Code is visible by default; rendering is configured not to execute live notebook code, so local LLM or GPU-heavy cells are not triggered during website builds.

Notebook Sequence

1. 01. Heterogeneous Treatment Effects
2. 02. CATE and Uplift Modeling
3. 03. Meta-Learners: S, T, X, R, and DR
4. 04. Causal Forests
5. 05. Double/Debiased Machine Learning
6. 06. Policy Learning and Treatment Targeting
7. 07. Off-Policy Evaluation
8. 08. Causal ML Model Validation

How To Read This Track

• Work through the notebooks in order if you want the full course arc.
• Treat each notebook as a lecture plus lab: read the discussion, inspect the code, and rerun locally when you want to experiment.
• For AI-heavy notebooks, expect some brittleness when live model calls are enabled; that instability is part of the course material rather than something hidden from the reader.

The .ipynb sources remain in the matching folder under notebooks/lectures/.