Direction Dependence Analysis
Foundations and Statistical Methods

Direction Dependence Analysis offers a coherent method to derive and test hypotheses about causal relationships and their directional effects.

Wolfgang Wiedermann (Author), Alexander von Eye (Author)

9781009381390, Cambridge University Press

Paperback / softback, published 14 August 2025

388 pages
22.8 x 15.1 x 2.1 cm, 0.57 kg

'Correlation is not causality! Does X cause Y, or Y cause X? Because correlations are symmetric for X and Y, we can't know… Until now! I first encountered Direction Dependence Analysis (DDA) in a paper by this book's authors. DDA is a brilliant idea: Use higher order moments to inform directionality. My thinking about correlations immediately became more nuanced and exciting. Read this book, and yours will too.' Joe Rodgers, Professor Emeritus of Psychology and Human Development, Vanderbilt University

While regression analysis is widely understood, it falls short in determining the causal direction of relationships in observational data. In this groundbreaking volume, Wiedermann and von Eye introduce Direction Dependence Analysis (DDA), a novel method that leverages variable information often overlooked by traditional techniques, such as higher-order moments like skewness and kurtosis. DDA reveals the asymmetry properties of regression and correlation, enabling researchers to evaluate competing causal hypotheses, assess the roles of variables in causal flows, and develop statistical methods for testing causal direction. This book provides a comprehensive formal description of DDA, illustrated with both artificial and real-world data examples. Additionally, readers will find free software implementations of DDA, making this an essential resource for researchers seeking to enhance their understanding of causal relationships in data analysis.

1. Introduction
2. The linear regression model
3. Asymmetry properties of distributions of observed variables
4. Asymmetry properties of error distributions
5. Independence properties of causes and errors
6. Direction of dependence under latent confounding
7. The integrated framework of Direction Dependence Analysis
8. Stability and sensitivity analyses
9. Extensions and applications
10. Statistical software
11. Concluding remarks.

Subject Areas: Psychological methodology [JMB]