04. Advanced Topics

Mediation, principal stratification, missing data, measurement error, transportability, interference, panels, discovery, Bayesian causal inference, and AI systems.

This track collects the advanced topics that separate routine effect estimation from mature causal practice: mechanisms, missingness, measurement, generalization, spillovers, panels, discovery, Bayesian workflows, and AI-system complications.

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. Mediation Analysis
2. 02. Principal Stratification
3. 03. Missing Data and Causal Inference
4. 04. Measurement Error
5. 05. Transportability and External Validity
6. 06. Interference and Spillovers
7. 07. Panel Data Complications
8. 08. Causal Discovery, With Caveats
9. 09. Bayesian Causal Inference
10. 10. Causal Inference with LLM and AI Systems

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/.