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
1 - Preliminaries
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
The shadow's cause
The Wayang Kulit is an ancient theatrical art, practised in Malaysia and throughout much of the Orient. The stories are often about battles between good and evil, as told in the great Hindu epics. What the audience actually sees are not actors, nor even puppets, but rather the shadows of puppets projected onto a canvas screen. Behind the screen is a light. The puppet master creates the action by manipulating the puppets and props so that they will intercept the light and cast shadows. As these shadows dance across the screen the audience must deduce the story from these two-dimensional projections of the hidden three-dimensional objects. Shadows, however, can be ambiguous. In order to infer the three-dimensional action, the shadows must be detailed, with sharp contours, and they must be placed in context.
Biologists are unwitting participants in nature's Shadow Play. These shadows are cast when the causal processes in nature are intercepted by our measurements. Like the audience at the Wayang Kulit, the biologist cannot simply peek behind the screen and directly observe the actual causal processes. All that can be directly observed are the consequences of these processes in the form of complicated patterns of association and independence in the data. As with shadows, these correlational patterns are incomplete – and potentially ambiguous – projections of the original causal processes. As with shadows, we can infer much about the underlying causal processes if we can learn to study their details, sharpen their contours, and especially if we can study them in context.
- Type
- Chapter
- Information
- Cause and Correlation in BiologyA User's Guide to Path Analysis, Structural Equations and Causal Inference, pp. 1 - 20Publisher: Cambridge University PressPrint publication year: 2000