A First Course in Causal Inference

Peng Ding (University of California Berkeley, U.S.A)

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Hardback

Forthcoming
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English

Chapman & Hall/CRC
09 July 2024

Psychological theory & schools of thought; Psychological methodology; Probability & statistics

Series: Chapman & Hall/CRC Texts in Statistical Science

The past decade has witnessed an explosion of interest in research and education in causal inference, due to its wide applications in biomedical research, social sciences, artificial intelligence etc. This textbook, based on the author's course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability theory, statistical inference, and linear and logistic regressions. It assumes minimal knowledge of causal inference, and reviews basic probability and statistics in the appendix. It covers causal inference from a statistical perspective and includes examples and applications from biostatistics and econometrics.

Key Features:

All R code and data sets available at Harvard Dataverse. Solutions manual available for instructors. Includes over 100 exercises.

This book is suitable for an advanced undergraduate or graduate-level course on causal inference, or postgraduate and PhD-level course in statistics and biostatistics departments.

By: Peng Ding (University of California Berkeley U.S.A)
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Dimensions: Height: 254mm, Width: 178mm,
ISBN: 9781032758626
ISBN 10: 1032758627
Series: Chapman & Hall/CRC Texts in Statistical Science
Pages: 448
Publication Date: 09 July 2024
Audience: College/higher education , Primary
Format: Hardback
Publisher's Status: Forthcoming

Peng Ding is an Associate Professor in the Department of Statistics at UC Berkeley. His research focuses on causal inference and its applications.

Reviews for A First Course in Causal Inference

"""This book offers a statistician’s perspective on causal inference. It provides an invaluable review of statistical paradoxes in causal inference from observational data, linking those paradoxes to Pearl’s directed acyclic graphs (DAGs). The overview of the literature on matching is the best that I’ve seen, and the inclusion of R code is a huge plus. The book would make a great introduction (and more) to advanced undergraduate and masters programs in statistics."" Professor Bryan Dowd, University of Minneapolis, U.S.A"