Determining directional dependency in causal associations.

Area of Science:

• Statistics
• Causal Inference
• Psychometrics

Background:

• Directional dependency is a statistical method used to infer the likely causal direction between two variables.
• Existing methods have limitations and potential issues, particularly concerning the interpretation of statistical properties and application in longitudinal studies.

Purpose of the Study:

• To critique and enhance the application of directional dependency for inferring causal associations.
• To propose improved methodological steps for determining directional dependency.
• To explore the integration of directional dependency in longitudinal data analysis and its accuracy under violated regression assumptions.

Main Methods:

• Critique of existing directional dependency methods, including commentary on skewness, kurtosis, and D'Agostino's K^2.
• Development of improved steps for directional dependency calculation.
• Examination of directional dependency in two-variable longitudinal data analysis.
• Assessment of accuracy when regression assumptions are violated.

Main Results:

• The study identifies specific issues with current directional dependency practices.
• Improved methods are proposed to address noted problems.
• Conditions under which directional dependency accurately suggests causal direction are delineated, including normality of regression error, limited correlation with unobserved variables, weak curvilinear relations, absence of outliers, and continuous variables.

Conclusions:

• Directional dependency, when applied with the proposed improvements and under specific statistical conditions, can be a valuable tool for inferring causal direction.
• The findings offer a more robust framework for utilizing directional dependency in statistical analysis, particularly within longitudinal research designs.