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Lecture notes. Written from the material of Berkeley Stat 156, licensed CC BY-NC 4.0. These are notes, not a transcript: the material has been reorganised and rewritten. This adaptation carries the same licence, and the original is linked above.

11. Course Overview: Causal Inference

What this covers

The only input for this chapter is the course syllabus page for Stat 156/256, which describes what the course covers rather than teaching any of it. It answers a narrower question than the other chapters in this book: not "what is causal inference" but "what is this course about, and how does it run". There is no lecture, no worked example and no mathematics behind this page, so none is manufactured here — this chapter records the course's own description of its scope and format, and nothing more.

What the course is about

Stat 156/256 is an introduction to causal inference built on the potential outcomes framework and causal diagrams. The syllabus names four topics the course covers: randomized experiments, observational studies, instrumental variables, and mediation analysis. It states that these methods are applied to fields such as medicine and public policy.

How the course runs

The syllabus describes a split between two kinds of session: lectures present concepts and methods, while lab sections focus on applications, derivations, and worked problems, using the statistical language R. The course is described as emphasizing both theory and data analysis.

Sources

  • Everything above comes from the course's syllabus page, notes input statistics/berkeley/stat156/fall-2024/syllabus.md — specifically its "About Stat 156/256" section, which is the only prose the source file contains (the two syllabus links it points to, for Stat 156 and Stat 256, were not supplied as inputs to this chapter).
  • No slides, transcript, or exercises were supplied for this chapter. The source material totals well under a thousand characters, and this chapter does not add topics, definitions, or examples beyond what that short description states.

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