Causal Inference (Part III, Michaelmas 2023)
This is a 16-lecture course on causal inference, the statistical science of drawing causal conclusions from experimental and non-experimental data.
Lectures
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Slides. [Updated: ]
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Hand-written notes. [Updated: ]
Example classes
- Time: 3:30pm on 2 Nov, 23 Nov, 18 Jan.
- Instructor: Jieru (Hera) Shi.
- Location: MR11.
- Example sheet 1. [First uploaded: ; Updated: ] Solution.
- Example sheet 2. [First uploaded: ; Updated: ] Solution.
- Example sheet 3. [Uploaded: ; Updated: ] Solution.
Office hour
- Time: 3pm-4pm on 19 Oct, 26 Oct, 9 Nov, 16 Nov, 30 Nov.
- Location: CMS D1.01.
Readings
The following books/articles are optional. I am providing a short (personal) verdict to help you navigate the literature.
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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.
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Causal Inference: What If by Miguel Hernán and James Robins [HR]. This book provides a comprehensive treatment for causal inference without and with models.
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Causality: Models, Reasoning, and Inference by Judea Pearl [Pearl]. A great book if you are interested in the philosophical debates in causal inference.
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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.
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Graphical Models by Steffen Lauritzen. A good reference for probabilistic graphical models.
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Observational Studies by Paul Rosenbaum. A good book for randomisation inference and sensitivity analysis.
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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.