Causal Inference (Part III, Michaelmas 2020)
This is a 16-lecture course on causal inference, the statistical science of drawing causal conclusions from experimental and non-experimental data.
Table of Contents
- Course syllabus.
- Time: Tuesday & Thursday, 11am–12.
- Location: Live-stream via Zoom (link available in Moodle).
- Office hour: I will stay on Zoom after each lecture to answer questions. I would also like to chat with every Part III student who is taking this course. Please sign up here for a 20 minute slot.
- Please email me if you find any mistakes or have any suggestions.
Full Lecture notes (Last updated: December 16, 2020).
Lecture recordings can be found in Moodle.
- Time: 13:30–15:00 on 28 October, 18 November, 2 December.
- Location: Zoom.
- Please submit your work for marking through Moodle.
- Example sheet 1.
- Example sheet 2.
- Example sheet 3.
- Example class 4: Present an applied article (follow this link).
- Sample exam questions.
The following books/articles are optional. I am providing a short (personal) verdict to help you navigate the literature.
Causal Inference for Statistics, Social, and Biomedical Sciences by Guido Imbens and Donald Rubin [IR]. This book provides a gentle introduction to potential outcomes and statistical methods for simple randomised experiments and observational studies with no unmeasured confounders.
Causal Inference: What If by Miguel Hernán and James Robins [HR]. This book provides a comprehensive treatment for causal inference without and with models.
Causality: Models, Reasoning, and Inference by Judea Pearl [Pearl]. A great book if you are interested in the philosophical debates in causal inference.
Statistical Models: Theory and Practice by David Freedman. A less technical textbook is well suited for someone who wants to learn the basic ideas in causal inference through practical examples.
Graphical Models by Steffen Lauritzen. A good reference for probabilistic graphical models.
Observational Studies by Paul Rosenbaum. A good book for randomisation inference and sensitivity analysis.
Mostly Harmless Econometrics: An Empiricist’s Companion by Joshua Angrist and Jörn-Steffen Pischke. Very clearly written book from an applied econometrics point of view, with a lot of useful intuitions.