Distinguishing cause from effect in psychological research: An independence-based approach under linear non-Gaussian models.

Distinguishing cause from effect - that is, determining whether x causes y (x → y) or, alternatively, whether y causes x (y → x) - is a primary research goal in many psychological research areas. Despite its importance, determining causal direction with observational data remains a difficult task. In this study, we introduce an independence-based approach for causal discovery between two variables of interest under a linear non-Gaussian model framework. We propose a two-step algorithm based on distance correlations that provides empirical conclusions on the causal directionality of effects under realistic conditions typically seen in psychological studies, that is, in the presence of hidden confounders. The performance of the proposed algorithm is evaluated using Monte-Carlo simulations. Findings suggest that the algorithm can effectively detect the causal direction between two variables of interest, even in the presence of weak hidden confounders. Moreover, distance correlations provide useful insights into the magnitude of hidden confounding. We provide an empirical example to demonstrate the application of our proposed approach and discuss practical implications and future directions.