Course Readings
Primary text
Hernán & Robins (2025)
Chapters 1–9 are the required reading for this course. The book is freely available from the link above. Abbreviated H&R on the schedule and in lecture notes.
Reading strategy
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.
Week-by-week readings
Week 1: How to ask a question in psychological science
Required: Hernán & Robins (2025), chapter 1. PDF
Optional: Briggs (2021) (history of measurement in psychology); Bandalos (2018) (psychometrics); Pearl & Mackenzie (2018) (accessible introduction to causal inference); Bulbulia (2024a) (causal questions in psychology).
Week 2: Causal diagrams — five elementary structures
Required: Hernán & Robins (2025), chapters 1–2 and 6. PDF
Optional: Bulbulia (2024a); Bulbulia (2024d) (experimental design and causal diagrams).
Week 3: Causal diagrams — the structures of confounding bias
Required: Hernán & Robins (2025), chapters 3 and 7. PDF
Optional: Bulbulia (2024a).
Week 4: Selection bias and measurement bias
Required: Hernán & Robins (2025), chapters 8 and 9. PDF
Optional: Bulbulia (2024c) (WEIRD samples and external validity); Bulbulia (2024b) (SWIGs and time-varying confounding).
Week 5: Average treatment effects
Required: Hernán & Robins (2025), chapters 1–2 (review identification assumptions). PDF
Week 6: Effect modification and CATE
Required: Hernán & Robins (2025), chapters 4–5. PDF
Optional: GRF documentation (causal forests, used in labs 6, 8, and 9).
General supplementary references
These are not required but provide additional depth on topics covered in the course.
- Neal (2020), chapters 1–2. Covers the same foundations as Hernán & Robins (2025) with a machine-learning orientation.
- Pearl et al. (2016). Compact introduction to the graphical (structural) approach to causation.
- Generalised Random Forests (GRF) website. Documentation, guides, and vignettes used in weeks 6, 8, and 9.
Bandalos, D. L. (2018). Measurement theory and applications for the social sciences. Guilford Publications.
Briggs, D. C. (2021). Historical and conceptual foundations of measurement in the human sciences: Credos and controversies. Routledge.
Bulbulia, J. A. (2024a). Methods in causal inference part 1: Causal diagrams and confounding. Evolutionary Human Sciences, 6, e40. https://doi.org/10.1017/ehs.2024.35
Bulbulia, J. A. (2024b). Methods in causal inference part 2: Interaction, mediation, and time-varying treatments. Evolutionary Human Sciences, 6, e41. https://doi.org/10.1017/ehs.2024.32
Bulbulia, J. A. (2024c). Methods in causal inference part 3: Measurement error and external validity threats. Evolutionary Human Sciences, 6, e42. https://doi.org/10.1017/ehs.2024.33
Bulbulia, J. A. (2024d). Methods in causal inference part 4: Confounding in experiments. Evolutionary Human Sciences, 6, e43. https://doi.org/10.1017/ehs.2024.34
Hernán, M. A., & Robins, J. M. (2025). Causal inference: What if. Chapman & Hall/CRC. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/
Neal, B. (2020). Introduction to causal inference from a machine learning perspective. Course Lecture Notes (Draft). https://www.bradyneal.com/Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf
Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: A primer. John Wiley & Sons.
Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic books.