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
9 - Exploratory Structural Equation Modelling
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
Previous chapters have dealt with inferential statistical tests; that is, you have a hypothesized causal model and want to know if the empirical data could reasonably have been generated from it. This chapter deals with the converse question: given the empirical data, what types of causal graphs could have generated it? This leads to exploratory methods of SEM. After explaining how to generate new hypotheses based on an original, but rejected, causal graph, this chapter explains two algorithms based on d-separation that do not require any pre-existing causal hypothesis: the causal inference algorithm and the vanishing tetrad algorithm. These are included in the pwSEM package.
Keywords
Information
- Type
- Chapter
- Information
- Cause and Correlation in BiologyA User's Guide to Path Analysis, Structural Equations and Causal Inference with R, pp. 302 - 352Publisher: Cambridge University PressPrint publication year: 2026
Accessibility standard: WCAG 2.0 A
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
The HTML of this chapter conforms to version 2.0 of the
Web Content Accessibility Guidelines
(WCAG), ensuring core accessibility principles are addressed and meets the basic
(A)
level of WCAG compliance, addressing essential accessibility barriers.
Content Navigation
Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Reading Order & Textual Equivalents
Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
Full alternative textual descriptions
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
Structural and Technical Features
ARIA roles provided
You gain clarity from ARIA (Accessible Rich Internet Applications) roles and attributes, as they help assistive technologies interpret how each part of the content functions.
You gain clarity from ARIA (Accessible Rich Internet Applications) roles and attributes, as they help assistive technologies interpret how each part of the content functions.