Cause and Correlation in Biology Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Preface to the second edition
- 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 equation model
- 7 Multigroup models, multilevel models and corrections for the non-independence of observations
- 8 Exploration, discovery and equivalence
- Appendix A cheat-sheet of useful R functions
- References
- Index
3 - Sewall Wright, path analysis and d-separation
Published online by Cambridge University Press: 05 April 2016
- Frontmatter
- Dedication
- Contents
- Preface
- Preface to the second edition
- 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 equation model
- 7 Multigroup models, multilevel models and corrections for the non-independence of observations
- 8 Exploration, discovery and equivalence
- Appendix A cheat-sheet of useful R functions
- References
- Index
Summary
A bit of history
The ideal method of science is the study of the direct influence of one condition on another in experiments in which all other possible causes of variation are eliminated. Unfortunately, causes of variation often seem to be beyond control. In the biological sciences, especially, one often has to deal with a group of characteristics or conditions which are correlated because of a complex of interacting, uncontrollable, and often obscure causes. The degree of correlation between two variables can be calculated with well-known methods, but when it is found it gives merely the resultant of all connecting paths of influence.
The present paper is an attempt to present a method of measuring the direct influence along each separate path in such a system and thus of finding the degree to which variation of a given effect is determined by each particular cause. The method depends on the combination of knowledge of the degrees of correlation among the variables in a system with such knowledge as may be possessed of the causal relations. In cases in which the causal relations are uncertain the method can be used to find the logical consequences of any particular hypothesis in regard to them.
So begins Sewall Wright's 1921 paper (Wright 1921), in which he describes his ‘method of path coefficients’. In fact, he invented this method while still in graduate school (Provine 1986) and had even used it without presenting its formal description in a paper published the previous year (Wright 1920). The 1920 paper used his new method to describe and measure the direct and indirect causal relationships that he had proposed to explain the patterns of inheritance of different colour patterns in guinea pigs. The paper came complete with a path diagram – i.e. a causal graph – in which actual drawings of the colour patterns of guinea pig coats were used instead of variable names.
Wright was one of the most influential evolutionary biologists of the twentieth century, being one of the founders of population genetics and intimately involved in the modern synthesis of evolutionary theory and genetics.
- Type
- Chapter
- Information
- Cause and Correlation in BiologyA User's Guide to Path Analysis, Structural Equations and Causal Inference with R, pp. 56 - 86Publisher: Cambridge University PressPrint publication year: 2016