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. PDF
Optional: Bulbulia (2024a); Bulbulia (2024d) (experimental design and causal diagrams).
Week 3: Causal diagrams — the structures of confounding bias
Required: Hernán & Robins (2025), chapter 6. PDF
Optional: Bulbulia (2024a).
Week 4: Selection bias and measurement bias
Required: Hernán & Robins (2025), chapters 6–9. 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; Cashin et al. (2025) — the TARGET (Transparent Reporting of Observational Studies Emulating a Target Trial) statement, a reporting checklist for studies that emulate a target trial.
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).
Week 7: In-class test 1
No new reading. Review Hernán & Robins (2025), chapters 1–9, the Causal Workflow, and the Test 1 Study Sheet.
Week 8: Heterogeneous treatment effects and machine learning
Required: GRF documentation — the Causal Forest article, the RATE article, and the Application: heterogeneity in clinical trials article. These are the operational references for the analysis workflow used in Labs 8–10 and the research report.
Optional: Wager & Athey (2018) (causal forests theory); VanderWeele et al. (2020) (measurement error and the potential outcomes framework, useful for the report).
Week 9: Resource allocation and policy trees
Required: GRF documentation — the Policy Learning article and the Qini curve article. Read alongside VanderWeele et al. (2020) and the lab's worked example.
Optional: Background on equity audits and governance considerations as named in the Week 9 lecture.
Week 10: Classical measurement theory from a causal perspective
Required: VanderWeele (2022) — Constructed Measures and Causal Inference: Towards a New Model of Measurement for Psychosocial Constructs. The paper sets out the structural-causal critique of classical measurement theory and motivates the multiple-versions-of-treatment perspective used in the lecture and the lab.
Optional: VanderWeele et al. (2020) (measurement error in the potential-outcomes framework); Hernán & Cole (2009) (causal diagrams and measurement bias).
Week 11: In-class test 2
No new reading. Review the Week 8, Week 9, and Week 10 lectures and the GRF documentation listed under Weeks 8–9.
Week 12: Student presentations
No new reading. Review the Presentation Rubric and the Reporting Guide.
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
Cashin, A. G., Hansford, H. J., Hernán, M. A., Swanson, S. A., Lee, H., Jones, M. D., Dahabreh, I. J., Dickerman, B. A., Egger, M., Garcia-Albeniz, X., et al. (2025). Transparent reporting of observational studies emulating a target trial—the TARGET statement. JAMA, 334(12), 1084–1093. https://doi.org/10.1001/jama.2025.13350
Hernán, M. A., & Cole, S. R. (2009). Invited commentary: Causal diagrams and measurement bias. American Journal of Epidemiology, 170(8), 959–962. https://doi.org/10.1093/aje/kwp293
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.
VanderWeele, T. J. (2022). Constructed measures and causal inference: Towards a new model of measurement for psychosocial constructs. Epidemiology, 33(1), 141–151. https://doi.org/10.1097/EDE.0000000000001434
VanderWeele, T. J., Mathur, M. B., & Chen, Y. (2020). Outcome-wide longitudinal designs for causal inference: A new template for empirical studies. Statistical Science, 35(3), 437–466.
Wager, S., & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523), 1228–1242. https://doi.org/10.1080/01621459.2017.1319839