03. Industry Applications

Applied causal inference for marketing, pricing, ranking, retention, rollouts, marketplaces, public-sector examples, and decision memos.

This track translates causal designs into the decisions companies actually make: incrementality, promotions, ranking, churn, feature launches, marketplaces, and executive readouts.

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. Marketing Incrementality
2. 02. Pricing and Promotions
3. 03. Recommendation Systems and Ranking
4. 04. Customer Retention and Churn Interventions
5. 05. Product Launches and Feature Rollouts
6. 06. Marketplace and Platform Interventions
7. 07. Health, Education, and Policy Applications
8. 08. From Causal Estimate to Decision Memo

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/.