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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.Uses simplistic language throughout to convey statistical testing methods for causal hypothesesWritten from the perspective of a practising biologist as a complete user's guide on testing causal hypothesesCombines the underlying philosophy, the theoretical background, and the practical implementation of structural equations, path analysis and causal inference to provide a completely up-to-date resource for students and biologists alike