Direction Dependence Analysis

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.

• Direction Dependence Analysis offers a robust statistical framework for testing causation hypotheses in variable relationships, empowering researchers to make informed conclusions about cause-and-effect dynamics
• This book presents a clear, comprehensive, and up-to-date introduction to Direction Dependence Analysis, providing researchers with essential theoretical insights and practical applications
• With a step-by-step guide and straightforward decision trees, the book simplifies the application of Direction Dependence Analysis, enabling researchers to conduct causal model selection without prior knowledge of the underlying causal structure that generated the data
• Accompanied by free R software, researchers can easily apply Direction Dependence Analysis to their own datasets, facilitating hands-on exploration of causal relationships
• The book explains key statistical principles relevant to causal machine learning, serving as an accessible introduction to the concepts underlying