Book contents
- Frontmatter
- 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
Preface
Published online by Cambridge University Press: 10 December 2009
- Frontmatter
- 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
Summary
This book describes a series of statistical methods for testing causal hypotheses using observational data – but it is not a statistics book. It describes a series of algorithms, derived from research in Artificial Intelligence, that can discover causal relationships from observational data – but it is not a book about Artificial Intelligence. It describes the logical and philosophical relationships between causality and probability distributions – but it is certainly not a book about the philosophy of statistics. Rather it is a user's guide, written for biologists, whose purpose is to allow the practising biologist to make use of these important new developments when causal questions can't be answered with randomised experiments.
I have written the book assuming that you have no previous training in these methods. If you have taken an introductory statistics course – even if it was longer ago than you want to acknowledge – and have managed to hold on to some of the basic notions of sampling and hypothesis testing using statistics, then you should be able to understand the material in this book. I recommend that you read each chapter through in its entirety, even if you don't feel that you have mastered all of the notions. This will at least give you a general feeling for the goals and vocabulary of each chapter. You can then go back and pay closer attention to the details.
The book is addressed to biologists, mostly because I am a practising biologist myself, but I hope that it will also be of interest to statisticians, scientists in other fields and even philosophers of science.
- Type
- Chapter
- Information
- Cause and Correlation in BiologyA User's Guide to Path Analysis, Structural Equations and Causal Inference, pp. xi - xiiPublisher: Cambridge University PressPrint publication year: 2000