Course Readings
Primary text
Hernan MA, Robins JM. Causal Inference: What If. Chapman & Hall/CRC, 2025.
Chapters 1–9 are the required reading for this course. The book is freely available from the link above.
Chapter guide
| Chapter | Topic | Course week(s) | What it covers |
|---|---|---|---|
| 1 | A definition of causal effect | w1–2 | Individual and average causal effects using potential outcomes notation. |
| 2 | Randomised experiments | w2 | How randomisation identifies causal effects and why experiments are the benchmark. |
| 3 | Observational studies | w3 | Conditions under which observational data can support causal inference. |
| 4 | Effect modification | w6 | How treatment effects vary across subgroups defined by baseline covariates. |
| 5 | Interaction | w4 | Distinguishing interaction from effect modification; additive vs. multiplicative scales. |
| 6 | Graphical representation of causal effects | w2–3 | Directed acyclic graphs (DAGs) and how they encode causal assumptions. |
| 7 | Confounding | w3–4 | Formal definition of confounding, the backdoor criterion, and adjustment strategies. |
| 8 | Selection bias | w4 | How conditioning on common effects (colliders) creates spurious associations. |
| 9 | Measurement bias | w10 | How measurement error distorts causal effect estimates. |
Read each chapter before the corresponding lecture week. The chapters are short (roughly 10–15 pages each) and written in accessible prose with worked examples. Focus on understanding the concepts rather than memorising notation.
Supplementary references
These are not required but provide additional depth on specific topics covered in the course.
- Neal B. Introduction to Causal Inference. 2020. Chapters 1–2. Covers the same foundations as Hernan and Robins with a machine-learning orientation.
- Pearl J, Glymour M, Jewell NP. Causal Inference in Statistics: A Primer. Wiley, 2016. Compact introduction to the graphical (structural) approach to causation.