01. Core Causal Inference

A complete applied foundation in causal questions, experiments, observational adjustment, and quasi-experimental designs.

This is the backbone sequence. It starts from causal questions and potential outcomes, then builds through experiments, adjustment-based observational designs, and quasi-experimental strategies used in product, policy, and industry settings.

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

01 Foundations

1. 01. What Is a Causal Question?
2. 02. Prediction vs Causal Inference
3. 03. Potential Outcomes and Counterfactuals
4. 04. Treatment, Outcome, Estimand, Population
5. 05. Average Treatment Effects: ATE, ATT, ATC, and CATE
6. 06. Core Identification Assumptions
7. 07. Causal DAGs and Graphical Assumptions
8. 08. Confounders, Mediators, Colliders, and Selection Bias

02 Experiments

1. 01. Randomized Experiments
2. 02. A/B Testing and Product Experimentation
3. 03. Power, MDE, Sample Size, and Practical Significance
4. 04. Guardrail Metrics and Multiple Testing
5. 05. Clustered Experiments
6. 06. Noncompliance, Intent-to-Treat, and Treatment-on-Treated
7. 07. Interference and Network Effects
8. 08. Experiment Readouts for Business Teams

03 Observational Adjustment

1. 01. Regression Adjustment
2. 02. Propensity Scores
3. 03. Matching
4. 04. Inverse Probability Weighting
5. 05. Covariate Balance Diagnostics
6. 06. Overlap and Trimming
7. 07. Doubly Robust Estimation
8. 08. Targeted Maximum Likelihood Estimation
9. 09. Sensitivity Analysis for Unobserved Confounding

04 Quasi Experiments

1. 01. Difference-in-Differences
2. 02. Event Studies
3. 03. Staggered Adoption and Modern DiD Issues
4. 04. Synthetic Control
5. 05. Regression Discontinuity
6. 06. Instrumental Variables
7. 07. Encouragement Designs
8. 08. Interrupted Time Series

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/.