One step at a time: A statistical approach for distinguishing mediators, confounders, and colliders using direction dependence analysis.
Year: 2023
Type: article
Abstract: relationship. Mediation, confounding, and colliding are three key third-variable effects that yield different theoretical and methodological implications for drawing causal conclusions. Commonly used covariance-based statistical methods, such as linear regression and structural equation modeling, cannot distinguish these effects in practice, however. In this study, we introduce a statistical approach for distinguishing mediators, confounders, colliders, and potential M-bias structures that uses higher-order moment information from the data. We propose a two-step procedure that uses the Hilbert-Schmidt independence criterion within the direction dependence analysis framework. Results from Monte Carlo simulations show that our proposed approach accurately recovers the true data-generating process of the third variable. We provide an empirical example to demonstrate the application of our proposed approach in psychological research. Finally, we discuss implications and future directions of our work. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Source: Psychological Methods
DOI: 10.1037/met0000619
Institutions: University of South Carolina, University of Missouri
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: University of South Carolina
OpenAlex ID: W4390060546