Cause and Correlation in Biology
A User's Guide to Path Analysis, Structural Equations and Causal Inference with R

A complete user's guide to structural equations explaining the underlying principals and practical implementation of these methods.

Bill Shipley (Author)

9781107442597, Cambridge University Press

Paperback / softback, published 18 April 2016

314 pages, 113 b/w illus. 22 tables
24.6 x 17.3 x 1.5 cm, 0.63 kg

'For a long time biologists have inferred causation only from carefully designed experiments. Shipley's book broadens horizons by showing how to use observational data to infer whether a causal model is plausible, and to estimate the variation in response due to competing causes.' David Warton, University of New South Wales, Sydney

Many problems in biology require an understanding of the relationships among variables in a multivariate causal context. Exploring such cause-effect relationships through a series of statistical methods, this book explains how to test causal hypotheses when randomised experiments cannot be performed. This completely revised and updated edition features detailed explanations for carrying out statistical methods using the popular and freely available R statistical language. Sections on d-sep tests, latent constructs that are common in biology, missing values, phylogenetic constraints, and multilevel models are also an important feature of this new edition. Written for biologists and using a minimum of statistical jargon, the concept of testing multivariate causal hypotheses using structural equations and path analysis is demystified. Assuming only a basic understanding of statistical analysis, this new edition is a valuable resource for both students and practising biologists.

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. Multigroup models, multilevel models, and corrections for non-independence of observations
8. Exploration, discovery and equivalence
Index.

Subject Areas: Applied ecology [RNC], The environment [RN], Earth sciences, geography, environment, planning [R], Probability & statistics [PBT], Mathematics [PB], Mathematics & science [P]