Cause and Correlation in BiologyA User's Guide to Path Analysis, Structural Equations and Causal Inference with R
Book contents
- Cause and Correlation in Biology
- Cause and Correlation in Biology
- Copyright page
- Dedication
- Contents
- Preface
- 1Cause from Correlation?
- 2From Cause to Correlation and Back
- 3Sewall Wright, Path Analysis, and d-Separation
- 4Covariance-Based SEM without Explicit Latent Variables
- 5Statistical Power, AIC Statistics, and Equivalent Models
- 6Piecewise SEM with Implicit Latent Variables
- 7Modelling Explicit Latent Variables in Covariance-Based SEM
- 8Multigroup and Multilevel Structural Equation Models
- 9Exploratory Structural Equation Modelling
- 10A Cheat Sheet of Important R Functions
- References
- Index
3 - Sewall Wright, Path Analysis, and d-Separation
Published online by Cambridge University Press: 18 July 2026
Book contents
- Cause and Correlation in Biology
- Cause and Correlation in Biology
- Copyright page
- Dedication
- Contents
- Preface
- 1Cause from Correlation?
- 2From Cause to Correlation and Back
- 3Sewall Wright, Path Analysis, and d-Separation
- 4Covariance-Based SEM without Explicit Latent Variables
- 5Statistical Power, AIC Statistics, and Equivalent Models
- 6Piecewise SEM with Implicit Latent Variables
- 7Modelling Explicit Latent Variables in Covariance-Based SEM
- 8Multigroup and Multilevel Structural Equation Models
- 9Exploratory Structural Equation Modelling
- 10A Cheat Sheet of Important R Functions
- References
- Index
Summary
A dsep test uses the notion of d-separation to produce an inferential statistical test comparing observed data to the hypothesized mechanism (piecewise SEM). This involves obtaining a subset of d-separation claims that logically imply all others (the union basis set), obtaining the null probability of each of the (conditional) independence claims implied by the d-separation claims in this basis set, and combining them. Two ways of obtaining the null probabilities of each of these d-separation claims are explained: using regression slopes and using the generalized covariance statistic. These are implemented in the ‘piecewiseSEM’ and ‘pwSEM’ packages of R respectively. The rules for interpreting and manipulating the resulting path coefficients are explained.
Keywords
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- Cause and Correlation in BiologyA User's Guide to Path Analysis, Structural Equations and Causal Inference with R, pp. 60 - 100Publisher: Cambridge University PressPrint publication year: 2026
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