This book goes beyond the truism that correlation does not imply causation and
explores the logical and methodological relationships between correlation and causation.
It presents a series of statistical methods that can test, and potentially discover,
cause-effect relationships between variables in situations in which it is not possible to
conduct randomised or experimentally controlled experiments. Many of these methods are
quite new and most are generally unknown to biologists. In addition to describing how to
conduct these statistical tests, the book also puts the methods into historical context
and explains when they can and cannot justifiably be used to test or discover causal
claims. Written in a conversational style that minimises technical jargon, the book is
aimed at practising biologists and advanced students, and assumes only a very basic
knowledge of introductory statistics.
Unique biological context
Worked examples in every chapter
Jargon-free conversational style
Contents
Preface; 1. Preliminaries; 2. From cause to correlation and back; 3. Sewall Wright,
path analysis and d-separation; 4. Path analysis and maximum likelihood; 5. Measurement
error and latent variables; 6. The structural equations model; 7. Nested models and
multilevel models; 8. Exploration, discovery and equivalence; Appendix; References; Index.
Reviews
- the perfect introduction to SEM. This book can be used as the primary text in a
SEM course given within any discipline, and can be used by scholars and researchers from
any area of science. Structural Equation Modeling
Addressing students and practising biologists, Shipley does a terrific job of making
mathematical ideas accessible Cause and Correlation in Biology is a nontechnical and
honest introduction to statistical methods for testing causal hypotheses. Johan
Paulsson, Nature Cell Biology
I highly recommend the book for those interested in multivariate approaches to
biology. Annals of Botany
I highly recommend the book by Shipley for those interested in multivariate approaches
to biology. Annals of Botany
314 pages