Causal Inference (Part III, Michaelmas 2021)

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

General information

• Course syllabus.

• Office hour: Wednesday at 2pm (if there is no example class) @ CMS, D1.01.

• Please email me or leave a comment below if you find any mistakes or have any questions.

Lectures

• Last year’s lecture notes.

• Corrected lecture notes.

• Lectures will be recorded and the recordings can be found on Moodle.

Example classes

• Time: 13:45–15:15 on 3 November, 17 November, 1 December, 19 January.
• Instructor: Tobias Freidling, who also provided the solutions below.
• Location: MR3.

• Example sheet 1 ( Solution).
• Example sheet 2 ( Solution).
• Example sheet 3 ( Solution).
• Example class 4: Present an applied article in here.
• Sample exam questions.

Readings

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.